Pandas Profiling Report

Dataset statistics

Number of variables	46
Number of observations	602
Missing cells	4461
Missing cells (%)	16.1%
Duplicate rows	0
Duplicate rows (%)	0.0%
Total size in memory	237.2 KiB
Average record size in memory	403.5 B

Variable types

Text	21
Categorical	7
DateTime	8
Numeric	8
Unsupported	2

Alerts

`degreeCategory2` is highly overall correlated with `fieldCategory2`	High correlation
`endorsement1` is highly overall correlated with `endorsement2` and 3 other fields	High correlation
`endorsement2` is highly overall correlated with `endorsement1` and 4 other fields	High correlation
`endorsement3` is highly overall correlated with `endorsement1` and 4 other fields	High correlation
`endorsement4` is highly overall correlated with `endorsement1` and 4 other fields	High correlation
`endorsement5` is highly overall correlated with `endorsement1` and 4 other fields	High correlation
`endorsement6` is highly overall correlated with `endorsement2` and 3 other fields	High correlation
`fieldCategory2` is highly overall correlated with `degreeCategory2`	High correlation
`jobTenureCategory` is highly overall correlated with `jobTenureYears`	High correlation
`jobTenureYears` is highly overall correlated with `jobTenureCategory`	High correlation
`company` is highly imbalanced (68.8%)	Imbalance
`jobLocation` has 137 (22.8%) missing values	Missing
`company2` has 19 (3.2%) missing values	Missing
`jobTitle2` has 20 (3.3%) missing values	Missing
`jobDateRange2` has 20 (3.3%) missing values	Missing
`jobStartedSince2` has 21 (3.5%) missing values	Missing
`school` has 30 (5.0%) missing values	Missing
`schoolUrl` has 101 (16.8%) missing values	Missing
`schoolDateRange` has 97 (16.1%) missing values	Missing
`schoolUrl2` has 297 (49.3%) missing values	Missing
`schoolDegree2` has 243 (40.4%) missing values	Missing
`schoolDateRange2` has 232 (38.5%) missing values	Missing
`allSkills` has 51 (8.5%) missing values	Missing
`skill1` has 51 (8.5%) missing values	Missing
`endorsement1` has 241 (40.0%) missing values	Missing
`skill2` has 52 (8.6%) missing values	Missing
`endorsement2` has 238 (39.5%) missing values	Missing
`skill3` has 53 (8.8%) missing values	Missing
`endorsement3` has 223 (37.0%) missing values	Missing
`skill4` has 57 (9.5%) missing values	Missing
`endorsement4` has 241 (40.0%) missing values	Missing
`skill5` has 64 (10.6%) missing values	Missing
`endorsement5` has 246 (40.9%) missing values	Missing
`skill6` has 81 (13.5%) missing values	Missing
`endorsement6` has 256 (42.5%) missing values	Missing
`error` has 602 (100.0%) missing values	Missing
`schoolStartDate` has 97 (16.1%) missing values	Missing
`schoolEndDate` has 97 (16.1%) missing values	Missing
`schoolStartDate2` has 232 (38.5%) missing values	Missing
`schoolEndDate2` has 232 (38.5%) missing values	Missing
`totalExperienceYears` has 93 (15.4%) missing values	Missing
`fullName` has unique values	Unique
`error` is an unsupported type, check if it needs cleaning or further analysis	Unsupported
`weighted_skills` is an unsupported type, check if it needs cleaning or further analysis	Unsupported
`totalExperienceYears` has 20 (3.3%) zeros	Zeros

Reproduction

Analysis started	2024-06-24 13:34:46.583494
Analysis finished	2024-06-24 13:35:15.349402
Duration	28.77 seconds
Software version	ydata-profiling v4.8.3
Download configuration	config.json

location
Text

Distinct	123
Distinct (%)	20.4%
Missing	0
Missing (%)	0.0%
Memory size	25.6 KiB

Length

Max length	49
Median length	46
Mean length	28.898671
Min length	6

Characters and Unicode

Total characters	17397
Distinct characters	57
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	91 ?
Unique (%)	15.1%

Sample

1st row	Greater Melbourne Area
2nd row	Melbourne, Victoria
3rd row	Greater Sydney Area
4th row	Melbourne, Victoria, Australia
5th row	Delhi, India

Value	Count	Frequency (%)
australia	345	15.9%
area	191	8.8%
greater	190	8.7%
sydney	156	7.2%
melbourne	145	6.7%
new	113	5.2%
south	113	5.2%
wales	108	5.0%
victoria	98	4.5%
australian	74	3.4%
Other values (162)	643	29.5%

Most occurring characters

Value	Count	Frequency (%)
a	2037	11.7%
r	1782	10.2%
e	1651	9.5%
	1574	9.0%
t	1132	6.5%
i	983	5.7%
l	865	5.0%
n	774	4.4%
u	739	4.2%
,	725	4.2%
Other values (47)	5135	29.5%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	17397	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
a	2037	11.7%
r	1782	10.2%
e	1651	9.5%
	1574	9.0%
t	1132	6.5%
i	983	5.7%
l	865	5.0%
n	774	4.4%
u	739	4.2%
,	725	4.2%
Other values (47)	5135	29.5%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	17397	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
a	2037	11.7%
r	1782	10.2%
e	1651	9.5%
	1574	9.0%
t	1132	6.5%
i	983	5.7%
l	865	5.0%
n	774	4.4%
u	739	4.2%
,	725	4.2%
Other values (47)	5135	29.5%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	17397	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
a	2037	11.7%
r	1782	10.2%
e	1651	9.5%
	1574	9.0%
t	1132	6.5%
i	983	5.7%
l	865	5.0%
n	774	4.4%
u	739	4.2%
,	725	4.2%
Other values (47)	5135	29.5%

fullName
Text

UNIQUE

Distinct	602
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Memory size	25.6 KiB

Length

Max length	51
Median length	23
Mean length	13.028239
Min length	7

Characters and Unicode

Total characters	7843
Distinct characters	64
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	602 ?
Unique (%)	100.0%

Sample

1st row	Will Adams
2nd row	Ammrith Adithya
3rd row	Prabin Agarwal
4th row	Priyanka Agarwal
5th row	Akansha Aggarwal

Value	Count	Frequency (%)
david	14	1.1%
nguyen	7	0.6%
andrew	7	0.6%
paul	7	0.6%
john	6	0.5%
gupta	6	0.5%
michael	6	0.5%
kumar	6	0.5%
sharma	6	0.5%
li	5	0.4%
Other values (983)	1188	94.4%

Most occurring characters

Value	Count	Frequency (%)
a	921	11.7%
	663	8.5%
n	588	7.5%
e	561	7.2%
i	506	6.5%
r	401	5.1%
o	358	4.6%
h	340	4.3%
l	315	4.0%
s	230	2.9%
Other values (54)	2960	37.7%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	7843	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
a	921	11.7%
	663	8.5%
n	588	7.5%
e	561	7.2%
i	506	6.5%
r	401	5.1%
o	358	4.6%
h	340	4.3%
l	315	4.0%
s	230	2.9%
Other values (54)	2960	37.7%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	7843	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
a	921	11.7%
	663	8.5%
n	588	7.5%
e	561	7.2%
i	506	6.5%
r	401	5.1%
o	358	4.6%
h	340	4.3%
l	315	4.0%
s	230	2.9%
Other values (54)	2960	37.7%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	7843	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
a	921	11.7%
	663	8.5%
n	588	7.5%
e	561	7.2%
i	506	6.5%
r	401	5.1%
o	358	4.6%
h	340	4.3%
l	315	4.0%
s	230	2.9%
Other values (54)	2960	37.7%

company
Categorical

IMBALANCE

Distinct	16
Distinct (%)	2.7%
Missing	0
Missing (%)	0.0%
Memory size	25.6 KiB

KPMG Australia	479
KPMG	62
KPMG US	15
KPMG UK	14
KPMG India	11
Other values (11)	21

Length

Max length	31
Median length	14
Mean length	12.616279
Min length	4

Characters and Unicode

Total characters	7595
Distinct characters	39
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	5 ?
Unique (%)	0.8%

Sample

1st row	KPMG Australia
2nd row	KPMG
3rd row	KPMG Australia
4th row	KPMG Australia
5th row	KPMG

Common Values

Value	Count	Frequency (%)
KPMG Australia	479	79.6%
KPMG	62	10.3%
KPMG US	15	2.5%
KPMG UK	14	2.3%
KPMG India	11	1.8%
KPMG Canada	4	0.7%
KPMG Vietnam	3	0.5%
KPMG China	3	0.5%
KPMG Nederland	2	0.3%
KPMG Global Services (KGS)	2	0.3%
Other values (6)	7	1.2%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
kpmg	602	52.2%
australia	479	41.5%
us	15	1.3%
uk	14	1.2%
india	11	1.0%
canada	4	0.3%
systems	3	0.3%
hands-on	3	0.3%
services	3	0.3%
china	3	0.3%
Other values (11)	16	1.4%

Most occurring characters

Value	Count	Frequency (%)
a	1000	13.2%
K	618	8.1%
G	606	8.0%
P	603	7.9%
M	602	7.9%
	551	7.3%
i	500	6.6%
s	493	6.5%
l	488	6.4%
t	487	6.4%
Other values (29)	1647	21.7%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	7595	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
a	1000	13.2%
K	618	8.1%
G	606	8.0%
P	603	7.9%
M	602	7.9%
	551	7.3%
i	500	6.6%
s	493	6.5%
l	488	6.4%
t	487	6.4%
Other values (29)	1647	21.7%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	7595	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
a	1000	13.2%
K	618	8.1%
G	606	8.0%
P	603	7.9%
M	602	7.9%
	551	7.3%
i	500	6.6%
s	493	6.5%
l	488	6.4%
t	487	6.4%
Other values (29)	1647	21.7%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	7595	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
a	1000	13.2%
K	618	8.1%
G	606	8.0%
P	603	7.9%
M	602	7.9%
	551	7.3%
i	500	6.6%
s	493	6.5%
l	488	6.4%
t	487	6.4%
Other values (29)	1647	21.7%

jobTitle
Categorical

Distinct	6
Distinct (%)	1.0%
Missing	0
Missing (%)	0.0%
Memory size	25.6 KiB

Associate Director	165
Manager	124
Senior Consultant	108
Director	93
Consultant	87

Length

Max length	18
Median length	17
Mean length	12.39701
Min length	7

Characters and Unicode

Total characters	7463
Distinct characters	19
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Senior Consultant
2nd row	Consultant
3rd row	Associate Director
4th row	Associate Director
5th row	Manager

Common Values

Value	Count	Frequency (%)
Associate Director	165	27.4%
Manager	124	20.6%
Senior Consultant	108	17.9%
Director	93	15.4%
Consultant	87	14.5%
Partner	25	4.2%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
director	258	29.5%
consultant	195	22.3%
associate	165	18.9%
manager	124	14.2%
senior	108	12.3%
partner	25	2.9%

Most occurring characters

Value	Count	Frequency (%)
t	838	11.2%
r	798	10.7%
o	726	9.7%
e	680	9.1%
n	647	8.7%
a	633	8.5%
i	531	7.1%
s	525	7.0%
c	423	5.7%
	273	3.7%
Other values (9)	1389	18.6%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	7463	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
t	838	11.2%
r	798	10.7%
o	726	9.7%
e	680	9.1%
n	647	8.7%
a	633	8.5%
i	531	7.1%
s	525	7.0%
c	423	5.7%
	273	3.7%
Other values (9)	1389	18.6%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	7463	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
t	838	11.2%
r	798	10.7%
o	726	9.7%
e	680	9.1%
n	647	8.7%
a	633	8.5%
i	531	7.1%
s	525	7.0%
c	423	5.7%
	273	3.7%
Other values (9)	1389	18.6%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	7463	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
t	838	11.2%
r	798	10.7%
o	726	9.7%
e	680	9.1%
n	647	8.7%
a	633	8.5%
i	531	7.1%
s	525	7.0%
c	423	5.7%
	273	3.7%
Other values (9)	1389	18.6%

jobLocation
Text

MISSING

Distinct	78
Distinct (%)	16.8%
Missing	137
Missing (%)	22.8%
Memory size	25.6 KiB

Length

Max length	59
Median length	47
Mean length	30.421505
Min length	5

Characters and Unicode

Total characters	14146
Distinct characters	53
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	40 ?
Unique (%)	8.6%

Sample

1st row	Melbourne, Victoria, Australia
2nd row	Melbourne, Victoria, Australia
3rd row	Sydney, New South Wales, Australia
4th row	Melbourne, Victoria, Australia
5th row	Delhi, India

Value	Count	Frequency (%)
australia	402	23.0%
sydney	135	7.7%
south	123	7.0%
new	118	6.8%
melbourne	117	6.7%
wales	115	6.6%
victoria	103	5.9%
·	64	3.7%
canberra	63	3.6%
australian	56	3.2%
Other values (98)	451	25.8%

Most occurring characters

Value	Count	Frequency (%)
a	1584	11.2%
	1282	9.1%
r	1135	8.0%
i	974	6.9%
e	941	6.7%
t	915	6.5%
l	814	5.8%
u	749	5.3%
,	727	5.1%
s	673	4.8%
Other values (43)	4352	30.8%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	14146	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
a	1584	11.2%
	1282	9.1%
r	1135	8.0%
i	974	6.9%
e	941	6.7%
t	915	6.5%
l	814	5.8%
u	749	5.3%
,	727	5.1%
s	673	4.8%
Other values (43)	4352	30.8%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	14146	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
a	1584	11.2%
	1282	9.1%
r	1135	8.0%
i	974	6.9%
e	941	6.7%
t	915	6.5%
l	814	5.8%
u	749	5.3%
,	727	5.1%
s	673	4.8%
Other values (43)	4352	30.8%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	14146	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
a	1584	11.2%
	1282	9.1%
r	1135	8.0%
i	974	6.9%
e	941	6.7%
t	915	6.5%
l	814	5.8%
u	749	5.3%
,	727	5.1%
s	673	4.8%
Other values (43)	4352	30.8%

jobDateRange
Text

Distinct	123
Distinct (%)	20.6%
Missing	5
Missing (%)	0.8%
Memory size	25.6 KiB

Length

Max length	27
Median length	18
Mean length	18.008375
Min length	11

Characters and Unicode

Total characters	10751
Distinct characters	38
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	70 ?
Unique (%)	11.7%

Sample

1st row	Oct 2020 - Feb 2022
2nd row	Feb 2024 - Present
3rd row	Mar 2022 - Present
4th row	May 2022 - Present
5th row	Sep 2023 - Present

Value	Count	Frequency (%)
	597	24.8%
present	568	23.5%
2022	183	7.6%
2023	175	7.3%
jul	119	4.9%
2021	109	4.5%
oct	106	4.4%
jan	97	4.0%
feb	89	3.7%
2024	65	2.7%
Other values (28)	304	12.6%

Most occurring characters

Value	Count	Frequency (%)
	1815	16.9%
2	1360	12.6%
e	1275	11.9%
n	691	6.4%
t	674	6.3%
0	651	6.1%
r	621	5.8%
-	597	5.6%
s	569	5.3%
P	568	5.3%
Other values (28)	1930	18.0%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	10751	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	1815	16.9%
2	1360	12.6%
e	1275	11.9%
n	691	6.4%
t	674	6.3%
0	651	6.1%
r	621	5.8%
-	597	5.6%
s	569	5.3%
P	568	5.3%
Other values (28)	1930	18.0%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	10751	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	1815	16.9%
2	1360	12.6%
e	1275	11.9%
n	691	6.4%
t	674	6.3%
0	651	6.1%
r	621	5.8%
-	597	5.6%
s	569	5.3%
P	568	5.3%
Other values (28)	1930	18.0%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	10751	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	1815	16.9%
2	1360	12.6%
e	1275	11.9%
n	691	6.4%
t	674	6.3%
0	651	6.1%
r	621	5.8%
-	597	5.6%
s	569	5.3%
P	568	5.3%
Other values (28)	1930	18.0%

jobStartedSince
Date

Distinct	95
Distinct (%)	15.9%
Missing	6
Missing (%)	1.0%
Memory size	25.6 KiB

Minimum	1996-11-01 00:00:00
Maximum	2024-05-01 00:00:00

Histogram

Histogram with fixed size bins (bins=50)

jobDuration
Text

Distinct	95
Distinct (%)	15.9%
Missing	6
Missing (%)	1.0%
Memory size	25.6 KiB

Length

Max length	13
Median length	12
Mean length	8.6073826
Min length	4

Characters and Unicode

Total characters	5130
Distinct characters	16
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	40 ?
Unique (%)	6.7%

Sample

1st row	1 yr 5 mos
2nd row	5 mos
3rd row	2 yrs 4 mos
4th row	2 yrs 2 mos
5th row	10 mos

Value	Count	Frequency (%)
mos	454	23.2%
yrs	304	15.5%
1	221	11.3%
2	211	10.8%
yr	196	10.0%
6	107	5.5%
5	99	5.1%
9	97	5.0%
3	80	4.1%
4	36	1.8%
Other values (14)	153	7.8%

Most occurring characters

Value	Count	Frequency (%)
	1362	26.5%
s	758	14.8%
y	500	9.7%
r	500	9.7%
m	479	9.3%
o	479	9.3%
1	319	6.2%
2	217	4.2%
6	109	2.1%
5	101	2.0%
Other values (6)	306	6.0%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	5130	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	1362	26.5%
s	758	14.8%
y	500	9.7%
r	500	9.7%
m	479	9.3%
o	479	9.3%
1	319	6.2%
2	217	4.2%
6	109	2.1%
5	101	2.0%
Other values (6)	306	6.0%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	5130	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	1362	26.5%
s	758	14.8%
y	500	9.7%
r	500	9.7%
m	479	9.3%
o	479	9.3%
1	319	6.2%
2	217	4.2%
6	109	2.1%
5	101	2.0%
Other values (6)	306	6.0%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	5130	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	1362	26.5%
s	758	14.8%
y	500	9.7%
r	500	9.7%
m	479	9.3%
o	479	9.3%
1	319	6.2%
2	217	4.2%
6	109	2.1%
5	101	2.0%
Other values (6)	306	6.0%

company2
Text

MISSING

Distinct	242
Distinct (%)	41.5%
Missing	19
Missing (%)	3.2%
Memory size	25.6 KiB

Length

Max length	63
Median length	52
Mean length	14.379074
Min length	2

Characters and Unicode

Total characters	8383
Distinct characters	82
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	208 ?
Unique (%)	35.7%

Sample

1st row	KPMG Australia
2nd row	Commonwealth Bank
3rd row	Accenture Australia
4th row	DXC Technology
5th row	KPMG

Value	Count	Frequency (%)
kpmg	298	24.1%
australia	268	21.6%
pwc	20	1.6%
	14	1.1%
group	13	1.1%
systems	13	1.1%
ltd	11	0.9%
deloitte	11	0.9%
india	10	0.8%
accenture	10	0.8%
Other values (390)	570	46.0%

Most occurring characters

Value	Count	Frequency (%)
a	800	9.5%
	655	7.8%
i	544	6.5%
t	520	6.2%
r	486	5.8%
s	471	5.6%
l	424	5.1%
u	383	4.6%
e	370	4.4%
P	363	4.3%
Other values (72)	3367	40.2%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	8383	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
a	800	9.5%
	655	7.8%
i	544	6.5%
t	520	6.2%
r	486	5.8%
s	471	5.6%
l	424	5.1%
u	383	4.6%
e	370	4.4%
P	363	4.3%
Other values (72)	3367	40.2%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	8383	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
a	800	9.5%
	655	7.8%
i	544	6.5%
t	520	6.2%
r	486	5.8%
s	471	5.6%
l	424	5.1%
u	383	4.6%
e	370	4.4%
P	363	4.3%
Other values (72)	3367	40.2%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	8383	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
a	800	9.5%
	655	7.8%
i	544	6.5%
t	520	6.2%
r	486	5.8%
s	471	5.6%
l	424	5.1%
u	383	4.6%
e	370	4.4%
P	363	4.3%
Other values (72)	3367	40.2%

jobTitle2
Text

MISSING

Distinct	388
Distinct (%)	66.7%
Missing	20
Missing (%)	3.3%
Memory size	25.6 KiB

Length

Max length	87
Median length	69
Mean length	24.35567
Min length	5

Characters and Unicode

Total characters	14175
Distinct characters	66
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	346 ?
Unique (%)	59.5%

Sample

1st row	Consultant
2nd row	Early Customer Engagement Specialist (Financial Assist Solutions)
3rd row	Manager
4th row	Senior Technical Consultant
5th row	Executive

Value	Count	Frequency (%)
	148	8.1%
manager	141	7.7%
consultant	138	7.5%
senior	100	5.5%
director	93	5.1%
associate	59	3.2%
technology	45	2.5%
lead	42	2.3%
data	37	2.0%
advisory	36	2.0%
Other values (341)	994	54.2%

Most occurring characters

Value	Count	Frequency (%)
	1264	8.9%
n	1228	8.7%
e	1226	8.6%
a	1145	8.1%
t	1066	7.5%
r	945	6.7%
o	905	6.4%
i	847	6.0%
s	709	5.0%
c	538	3.8%
Other values (56)	4302	30.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	14175	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	1264	8.9%
n	1228	8.7%
e	1226	8.6%
a	1145	8.1%
t	1066	7.5%
r	945	6.7%
o	905	6.4%
i	847	6.0%
s	709	5.0%
c	538	3.8%
Other values (56)	4302	30.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	14175	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	1264	8.9%
n	1228	8.7%
e	1226	8.6%
a	1145	8.1%
t	1066	7.5%
r	945	6.7%
o	905	6.4%
i	847	6.0%
s	709	5.0%
c	538	3.8%
Other values (56)	4302	30.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	14175	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	1264	8.9%
n	1228	8.7%
e	1226	8.6%
a	1145	8.1%
t	1066	7.5%
r	945	6.7%
o	905	6.4%
i	847	6.0%
s	709	5.0%
c	538	3.8%
Other values (56)	4302	30.3%

jobDateRange2
Text

MISSING

Distinct	473
Distinct (%)	81.3%
Missing	20
Missing (%)	3.3%
Memory size	25.6 KiB

Length

Max length	28
Median length	19
Mean length	18.785223
Min length	8

Characters and Unicode

Total characters	10933
Distinct characters	38
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	402 ?
Unique (%)	69.1%

Sample

1st row	Sep 2018 - Oct 2020
2nd row	Mar 2020 - Aug 2020
3rd row	Oct 2016 - Feb 2022
4th row	Sep 2021 - May 2022
5th row	Sep 2022 - Sep 2023

Value	Count	Frequency (%)
	581	20.5%
2022	283	10.0%
2021	251	8.9%
oct	164	5.8%
feb	155	5.5%
2023	155	5.5%
jul	118	4.2%
sep	115	4.1%
jan	110	3.9%
jun	96	3.4%
Other values (32)	801	28.3%

Most occurring characters

Value	Count	Frequency (%)
	2247	20.6%
2	2201	20.1%
0	1225	11.2%
-	581	5.3%
1	514	4.7%
e	471	4.3%
J	324	3.0%
u	276	2.5%
n	273	2.5%
c	231	2.1%
Other values (28)	2590	23.7%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	10933	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	2247	20.6%
2	2201	20.1%
0	1225	11.2%
-	581	5.3%
1	514	4.7%
e	471	4.3%
J	324	3.0%
u	276	2.5%
n	273	2.5%
c	231	2.1%
Other values (28)	2590	23.7%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	10933	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	2247	20.6%
2	2201	20.1%
0	1225	11.2%
-	581	5.3%
1	514	4.7%
e	471	4.3%
J	324	3.0%
u	276	2.5%
n	273	2.5%
c	231	2.1%
Other values (28)	2590	23.7%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	10933	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	2247	20.6%
2	2201	20.1%
0	1225	11.2%
-	581	5.3%
1	514	4.7%
e	471	4.3%
J	324	3.0%
u	276	2.5%
n	273	2.5%
c	231	2.1%
Other values (28)	2590	23.7%

jobStartedSince2
Date

MISSING

Distinct	128
Distinct (%)	22.0%
Missing	21
Missing (%)	3.5%
Memory size	25.6 KiB

Minimum	1994-04-01 00:00:00
Maximum	2024-02-01 00:00:00

Histogram

Histogram with fixed size bins (bins=50)

school
Text

MISSING

Distinct	248
Distinct (%)	43.4%
Missing	30
Missing (%)	5.0%
Memory size	25.6 KiB

Length

Max length	127
Median length	58
Mean length	25.26049
Min length	3

Characters and Unicode

Total characters	14449
Distinct characters	72
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	200 ?
Unique (%)	35.0%

Sample

1st row	Monash University
2nd row	Monash University
3rd row	Institute of Chartered Accountant of India
4th row	College Of Engineering Roorkee
5th row	The Institute of Chartered Accountants of India

Value	Count	Frequency (%)
university	372	19.3%
of	258	13.4%
technology	70	3.6%
the	68	3.5%
unsw	56	2.9%
sydney	47	2.4%
school	47	2.4%
monash	39	2.0%
institute	37	1.9%
queensland	34	1.8%
Other values (399)	901	46.7%

Most occurring characters

Value	Count	Frequency (%)
	1357	9.4%
i	1205	8.3%
e	1199	8.3%
n	1154	8.0%
o	800	5.5%
t	785	5.4%
s	752	5.2%
r	748	5.2%
a	741	5.1%
y	570	3.9%
Other values (62)	5138	35.6%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	14449	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	1357	9.4%
i	1205	8.3%
e	1199	8.3%
n	1154	8.0%
o	800	5.5%
t	785	5.4%
s	752	5.2%
r	748	5.2%
a	741	5.1%
y	570	3.9%
Other values (62)	5138	35.6%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	14449	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	1357	9.4%
i	1205	8.3%
e	1199	8.3%
n	1154	8.0%
o	800	5.5%
t	785	5.4%
s	752	5.2%
r	748	5.2%
a	741	5.1%
y	570	3.9%
Other values (62)	5138	35.6%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	14449	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	1357	9.4%
i	1205	8.3%
e	1199	8.3%
n	1154	8.0%
o	800	5.5%
t	785	5.4%
s	752	5.2%
r	748	5.2%
a	741	5.1%
y	570	3.9%
Other values (62)	5138	35.6%

schoolUrl
Text

MISSING

Distinct	193
Distinct (%)	38.5%
Missing	101
Missing (%)	16.8%
Memory size	25.6 KiB

Length

Max length	42
Median length	41
Mean length	39.307385
Min length	38

Characters and Unicode

Total characters	19693
Distinct characters	29
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	149 ?
Unique (%)	29.7%

Sample

1st row	https://www.linkedin.com/company/5663/
2nd row	https://www.linkedin.com/company/5663/
3rd row	https://www.linkedin.com/company/1968486/
4th row	https://www.linkedin.com/company/739903/
5th row	https://www.linkedin.com/company/363736/

Value	Count	Frequency (%)
https://www.linkedin.com/company/6096	45	9.0%
https://www.linkedin.com/company/5663	38	7.6%
https://www.linkedin.com/company/166678	26	5.2%
https://www.linkedin.com/company/162587	25	5.0%
https://www.linkedin.com/company/5885	21	4.2%
https://www.linkedin.com/company/5677	18	3.6%
https://www.linkedin.com/company/165624	18	3.6%
https://www.linkedin.com/company/166676	15	3.0%
https://www.linkedin.com/company/166664	13	2.6%
https://www.linkedin.com/company/7763	12	2.4%
Other values (183)	270	53.9%

Most occurring characters

Value	Count	Frequency (%)
/	2505	12.7%
w	1503	7.6%
n	1503	7.6%
c	1002	5.1%
t	1002	5.1%
p	1002	5.1%
.	1002	5.1%
i	1002	5.1%
m	1002	5.1%
o	1002	5.1%
Other values (19)	7168	36.4%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	19693	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
/	2505	12.7%
w	1503	7.6%
n	1503	7.6%
c	1002	5.1%
t	1002	5.1%
p	1002	5.1%
.	1002	5.1%
i	1002	5.1%
m	1002	5.1%
o	1002	5.1%
Other values (19)	7168	36.4%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	19693	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
/	2505	12.7%
w	1503	7.6%
n	1503	7.6%
c	1002	5.1%
t	1002	5.1%
p	1002	5.1%
.	1002	5.1%
i	1002	5.1%
m	1002	5.1%
o	1002	5.1%
Other values (19)	7168	36.4%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	19693	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
/	2505	12.7%
w	1503	7.6%
n	1503	7.6%
c	1002	5.1%
t	1002	5.1%
p	1002	5.1%
.	1002	5.1%
i	1002	5.1%
m	1002	5.1%
o	1002	5.1%
Other values (19)	7168	36.4%

schoolDateRange
Text

MISSING

Distinct	234
Distinct (%)	46.3%
Missing	97
Missing (%)	16.1%
Memory size	25.6 KiB

Length

Max length	19
Median length	11
Mean length	11.726733
Min length	4

Characters and Unicode

Total characters	5922
Distinct characters	34
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	139 ?
Unique (%)	27.5%

Sample

1st row	2012 - 2016
2nd row	2019 - 2023
3rd row	1999 - 2004
4th row	2002 - 2006
5th row	2012 - 2018

Value	Count	Frequency (%)
	486	30.3%
2021	68	4.2%
2020	63	3.9%
2018	62	3.9%
2019	60	3.7%
2017	54	3.4%
2015	48	3.0%
2022	45	2.8%
2012	45	2.8%
2016	44	2.7%
Other values (54)	627	39.1%

Most occurring characters

Value	Count	Frequency (%)
2	1210	20.4%
0	1200	20.3%
	1097	18.5%
1	663	11.2%
-	486	8.2%
9	332	5.6%
8	131	2.2%
7	107	1.8%
6	85	1.4%
4	85	1.4%
Other values (24)	526	8.9%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	5922	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
2	1210	20.4%
0	1200	20.3%
	1097	18.5%
1	663	11.2%
-	486	8.2%
9	332	5.6%
8	131	2.2%
7	107	1.8%
6	85	1.4%
4	85	1.4%
Other values (24)	526	8.9%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	5922	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
2	1210	20.4%
0	1200	20.3%
	1097	18.5%
1	663	11.2%
-	486	8.2%
9	332	5.6%
8	131	2.2%
7	107	1.8%
6	85	1.4%
4	85	1.4%
Other values (24)	526	8.9%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	5922	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
2	1210	20.4%
0	1200	20.3%
	1097	18.5%
1	663	11.2%
-	486	8.2%
9	332	5.6%
8	131	2.2%
7	107	1.8%
6	85	1.4%
4	85	1.4%
Other values (24)	526	8.9%

schoolUrl2
Text

MISSING

Distinct	192
Distinct (%)	63.0%
Missing	297
Missing (%)	49.3%
Memory size	25.6 KiB

Length

Max length	43
Median length	42
Mean length	39.783607
Min length	38

Characters and Unicode

Total characters	12134
Distinct characters	29
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	160 ?
Unique (%)	52.5%

Sample

1st row	https://www.linkedin.com/company/5663/
2nd row	https://www.linkedin.com/company/633270/
3rd row	https://www.linkedin.com/company/9584148/
4th row	https://www.linkedin.com/company/15109269/
5th row	https://www.linkedin.com/company/5663/

Value	Count	Frequency (%)
https://www.linkedin.com/company/6096	12	3.9%
https://www.linkedin.com/company/5885	10	3.3%
https://www.linkedin.com/company/162587	10	3.3%
https://www.linkedin.com/company/165624	10	3.3%
https://www.linkedin.com/company/5663	10	3.3%
https://www.linkedin.com/company/166676	8	2.6%
https://www.linkedin.com/company/5677	7	2.3%
https://www.linkedin.com/company/19034	7	2.3%
https://www.linkedin.com/company/7763	5	1.6%
https://www.linkedin.com/company/166678	5	1.6%
Other values (182)	221	72.5%

Most occurring characters

Value	Count	Frequency (%)
/	1525	12.6%
w	915	7.5%
n	915	7.5%
c	610	5.0%
p	610	5.0%
.	610	5.0%
m	610	5.0%
i	610	5.0%
o	610	5.0%
t	610	5.0%
Other values (19)	4509	37.2%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	12134	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
/	1525	12.6%
w	915	7.5%
n	915	7.5%
c	610	5.0%
p	610	5.0%
.	610	5.0%
m	610	5.0%
i	610	5.0%
o	610	5.0%
t	610	5.0%
Other values (19)	4509	37.2%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	12134	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
/	1525	12.6%
w	915	7.5%
n	915	7.5%
c	610	5.0%
p	610	5.0%
.	610	5.0%
m	610	5.0%
i	610	5.0%
o	610	5.0%
t	610	5.0%
Other values (19)	4509	37.2%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	12134	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
/	1525	12.6%
w	915	7.5%
n	915	7.5%
c	610	5.0%
p	610	5.0%
.	610	5.0%
m	610	5.0%
i	610	5.0%
o	610	5.0%
t	610	5.0%
Other values (19)	4509	37.2%

schoolDegree2
Text

MISSING

Distinct	345
Distinct (%)	96.1%
Missing	243
Missing (%)	40.4%
Memory size	25.6 KiB

Length

Max length	112
Median length	72
Mean length	42.420613
Min length	3

Characters and Unicode

Total characters	15229
Distinct characters	71
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	336 ?
Unique (%)	93.6%

Sample

1st row	Bachelor's degree, Business (Marketing) (International Business)
2nd row	VCE
3rd row	Bachelor of Commerce (B.Com.), Accountancy
4th row	SSC, PCM
5th row	BCA, Computer Applications

Value	Count	Frequency (%)
of	152	8.0%
bachelor	108	5.7%
	71	3.7%
and	71	3.7%
business	70	3.7%
science	55	2.9%
engineering	48	2.5%
management	48	2.5%
information	47	2.5%
degree	47	2.5%
Other values (393)	1177	62.1%

Most occurring characters

Value	Count	Frequency (%)
	1541	10.1%
e	1381	9.1%
n	1189	7.8%
o	1042	6.8%
a	974	6.4%
i	966	6.3%
c	774	5.1%
r	769	5.0%
t	724	4.8%
s	640	4.2%
Other values (61)	5229	34.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	15229	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	1541	10.1%
e	1381	9.1%
n	1189	7.8%
o	1042	6.8%
a	974	6.4%
i	966	6.3%
c	774	5.1%
r	769	5.0%
t	724	4.8%
s	640	4.2%
Other values (61)	5229	34.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	15229	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	1541	10.1%
e	1381	9.1%
n	1189	7.8%
o	1042	6.8%
a	974	6.4%
i	966	6.3%
c	774	5.1%
r	769	5.0%
t	724	4.8%
s	640	4.2%
Other values (61)	5229	34.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	15229	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	1541	10.1%
e	1381	9.1%
n	1189	7.8%
o	1042	6.8%
a	974	6.4%
i	966	6.3%
c	774	5.1%
r	769	5.0%
t	724	4.8%
s	640	4.2%
Other values (61)	5229	34.3%

schoolDateRange2
Text

MISSING

Distinct	199
Distinct (%)	53.8%
Missing	232
Missing (%)	38.5%
Memory size	25.6 KiB

Length

Max length	19
Median length	11
Mean length	11.321622
Min length	4

Characters and Unicode

Total characters	4189
Distinct characters	34
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	115 ?
Unique (%)	31.1%

Sample

1st row	2012 - 2016
2nd row	2015 - 2018
3rd row	1999 - 2002
4th row	1999 - 2002
5th row	2001 - 2004

Value	Count	Frequency (%)
	351	30.9%
2014	56	4.9%
2016	53	4.7%
2018	47	4.1%
2012	44	3.9%
2013	40	3.5%
2015	38	3.3%
2019	31	2.7%
2011	29	2.6%
2008	29	2.6%
Other values (52)	417	36.7%

Most occurring characters

Value	Count	Frequency (%)
0	883	21.1%
2	766	18.3%
	765	18.3%
1	546	13.0%
-	351	8.4%
9	223	5.3%
8	108	2.6%
4	87	2.1%
6	78	1.9%
5	67	1.6%
Other values (24)	315	7.5%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	4189	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
0	883	21.1%
2	766	18.3%
	765	18.3%
1	546	13.0%
-	351	8.4%
9	223	5.3%
8	108	2.6%
4	87	2.1%
6	78	1.9%
5	67	1.6%
Other values (24)	315	7.5%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	4189	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
0	883	21.1%
2	766	18.3%
	765	18.3%
1	546	13.0%
-	351	8.4%
9	223	5.3%
8	108	2.6%
4	87	2.1%
6	78	1.9%
5	67	1.6%
Other values (24)	315	7.5%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	4189	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
0	883	21.1%
2	766	18.3%
	765	18.3%
1	546	13.0%
-	351	8.4%
9	223	5.3%
8	108	2.6%
4	87	2.1%
6	78	1.9%
5	67	1.6%
Other values (24)	315	7.5%

allSkills
Text

MISSING

Distinct	551
Distinct (%)	100.0%
Missing	51
Missing (%)	8.5%
Memory size	25.6 KiB

Length

Max length	869
Median length	453
Mean length	358.52087
Min length	15

Characters and Unicode

Total characters	197545
Distinct characters	73
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	551 ?
Unique (%)	100.0%

Sample

1st row	Management Consulting, Presentations, Microsoft Dynamics CRM, Scrum, Critical Thinking, Microsoft PowerPoint
2nd row	FICO, ECC, SAP R/3, SAP Implementation, SAP, ERP, SAP FICO, Requirements Analysis, SAP FI, SAP ERP, Business Analysis, IDOC, SAP Products, Consulting, Integration, Financial Reporting, Team Management, Vendor Management
3rd row	MorphX, X++, Microsoft Dynamics ERP, Oracle 10g, Red Hat Linux, Telecom BSS, Microsoft Dynamics AX 4.0, 2009, 2012, 2012 R2, Enterprise Portals, Oracle, Microsoft Dynamics, SSRS, ERP, Solution Architecture, Business Process, Requirements Analysis, SQL, .NET, Business Analysis, Microsoft SQL Server, Enterprise Resource Planning (ERP)
4th row	External Audits, Auditing, Tax, Financial Audits, Financial Statement Auditing
5th row	Business Process Improvement, Technical Specs, Process Modeling, Problem Solving, Communication, Strategy, Technical Architecture, Business Requirements, Architecture, Agile Methodologies, PL/SQL, Java, Testing, Telecommunications, Requirements Analysis, Service Delivery, Integration, User Acceptance Testing, SDP, Solution Architecture, Test Planning, Software Project Management, Test Automation, Software Development, Software Quality Assurance, Oracle, OSS, Open Source Software, Linux, WebLogic, Quality Center, Weblogic, Unix, SQL, JIRA, SoapUI, HP Quality Center

Value	Count	Frequency (%)
management	1574	7.3%
business	952	4.4%
microsoft	639	3.0%
analysis	600	2.8%
project	512	2.4%
data	428	2.0%
process	351	1.6%
strategy	333	1.6%
it	318	1.5%
development	244	1.1%
Other values (1844)	15467	72.2%

Most occurring characters

Value	Count	Frequency (%)
	20867	10.6%
e	18186	9.2%
n	14281	7.2%
a	12644	6.4%
i	11850	6.0%
t	11757	6.0%
,	10937	5.5%
s	10374	5.3%
o	9607	4.9%
r	9436	4.8%
Other values (63)	67606	34.2%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	197545	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	20867	10.6%
e	18186	9.2%
n	14281	7.2%
a	12644	6.4%
i	11850	6.0%
t	11757	6.0%
,	10937	5.5%
s	10374	5.3%
o	9607	4.9%
r	9436	4.8%
Other values (63)	67606	34.2%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	197545	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	20867	10.6%
e	18186	9.2%
n	14281	7.2%
a	12644	6.4%
i	11850	6.0%
t	11757	6.0%
,	10937	5.5%
s	10374	5.3%
o	9607	4.9%
r	9436	4.8%
Other values (63)	67606	34.2%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	197545	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	20867	10.6%
e	18186	9.2%
n	14281	7.2%
a	12644	6.4%
i	11850	6.0%
t	11757	6.0%
,	10937	5.5%
s	10374	5.3%
o	9607	4.9%
r	9436	4.8%
Other values (63)	67606	34.2%

skill1
Text

MISSING

Distinct	318
Distinct (%)	57.7%
Missing	51
Missing (%)	8.5%
Memory size	25.6 KiB

Length

Max length	61
Median length	38
Mean length	16.417423
Min length	1

Characters and Unicode

Total characters	9046
Distinct characters	66
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	236 ?
Unique (%)	42.8%

Sample

1st row	Management Consulting
2nd row	FICO
3rd row	MorphX
4th row	External Audits
5th row	Business Process Improvement

Value	Count	Frequency (%)
management	64	5.9%
business	54	5.0%
microsoft	40	3.7%
analysis	35	3.2%
project	26	2.4%
data	25	2.3%
strategy	24	2.2%
it	20	1.8%
consulting	19	1.7%
office	18	1.7%
Other values (326)	761	70.1%

Most occurring characters

Value	Count	Frequency (%)
e	886	9.8%
n	729	8.1%
i	649	7.2%
a	621	6.9%
t	605	6.7%
s	567	6.3%
	535	5.9%
o	511	5.6%
r	476	5.3%
c	320	3.5%
Other values (56)	3147	34.8%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	9046	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
e	886	9.8%
n	729	8.1%
i	649	7.2%
a	621	6.9%
t	605	6.7%
s	567	6.3%
	535	5.9%
o	511	5.6%
r	476	5.3%
c	320	3.5%
Other values (56)	3147	34.8%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	9046	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
e	886	9.8%
n	729	8.1%
i	649	7.2%
a	621	6.9%
t	605	6.7%
s	567	6.3%
	535	5.9%
o	511	5.6%
r	476	5.3%
c	320	3.5%
Other values (56)	3147	34.8%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	9046	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
e	886	9.8%
n	729	8.1%
i	649	7.2%
a	621	6.9%
t	605	6.7%
s	567	6.3%
	535	5.9%
o	511	5.6%
r	476	5.3%
c	320	3.5%
Other values (56)	3147	34.8%

endorsement1
Real number (ℝ)

HIGH CORRELATION MISSING

Distinct	60
Distinct (%)	16.6%
Missing	241
Missing (%)	40.0%
Infinite	0
Infinite (%)	0.0%
Mean	23.944598

Minimum	1
Maximum	400
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	25.6 KiB

Quantile statistics

Minimum	1
5-th percentile	1
Q1	2
median	7
Q3	26
95-th percentile	65
Maximum	400
Range	399
Interquartile range (IQR)	24

Descriptive statistics

Standard deviation	55.741589
Coefficient of variation (CV)	2.32794
Kurtosis	30.62305
Mean	23.944598
Median Absolute Deviation (MAD)	6
Skewness	5.3686813
Sum	8644
Variance	3107.1247
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
1	60	10.0%
2	37	6.1%
3	31	5.1%
5	19	3.2%
4	14	2.3%
6	11	1.8%
7	10	1.7%
21	9	1.5%
31	9	1.5%
10	8	1.3%
Other values (50)	153	25.4%
(Missing)	241	40.0%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
1	60	10.0%
2	37	6.1%
3	31	5.1%
4	14	2.3%
5	19	3.2%
6	11	1.8%
7	10	1.7%
8	8	1.3%
9	8	1.3%
10	8	1.3%

Value	Count	Frequency (%)
400	1	0.2%
365	7	1.2%
126	1	0.2%
99	1	0.2%
97	1	0.2%
96	1	0.2%
87	1	0.2%
76	1	0.2%
72	1	0.2%
66	2	0.3%

skill2
Text

MISSING

Distinct	342
Distinct (%)	62.2%
Missing	52
Missing (%)	8.6%
Memory size	25.6 KiB

Length

Max length	59
Median length	31
Mean length	15.687273
Min length	1

Characters and Unicode

Total characters	8628
Distinct characters	61
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	259 ?
Unique (%)	47.1%

Sample

1st row	Presentations
2nd row	ECC
3rd row	X++
4th row	Auditing
5th row	Technical Specs

Value	Count	Frequency (%)
management	53	5.0%
business	40	3.8%
microsoft	35	3.3%
it	26	2.5%
data	25	2.4%
analysis	25	2.4%
strategy	22	2.1%
project	21	2.0%
architecture	15	1.4%
sap	13	1.2%
Other values (354)	783	74.0%

Most occurring characters

Value	Count	Frequency (%)
e	858	9.9%
n	644	7.5%
a	606	7.0%
t	583	6.8%
i	575	6.7%
	508	5.9%
r	493	5.7%
s	491	5.7%
o	483	5.6%
c	313	3.6%
Other values (51)	3074	35.6%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	8628	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
e	858	9.9%
n	644	7.5%
a	606	7.0%
t	583	6.8%
i	575	6.7%
	508	5.9%
r	493	5.7%
s	491	5.7%
o	483	5.6%
c	313	3.6%
Other values (51)	3074	35.6%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	8628	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
e	858	9.9%
n	644	7.5%
a	606	7.0%
t	583	6.8%
i	575	6.7%
	508	5.9%
r	493	5.7%
s	491	5.7%
o	483	5.6%
c	313	3.6%
Other values (51)	3074	35.6%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	8628	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
e	858	9.9%
n	644	7.5%
a	606	7.0%
t	583	6.8%
i	575	6.7%
	508	5.9%
r	493	5.7%
s	491	5.7%
o	483	5.6%
c	313	3.6%
Other values (51)	3074	35.6%

endorsement2
Real number (ℝ)

HIGH CORRELATION MISSING

Distinct	57
Distinct (%)	15.7%
Missing	238
Missing (%)	39.5%
Infinite	0
Infinite (%)	0.0%
Mean	17.071429

Minimum	1
Maximum	400
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	25.6 KiB

Quantile statistics

Minimum	1
5-th percentile	1
Q1	2
median	7
Q3	19.25
95-th percentile	52
Maximum	400
Range	399
Interquartile range (IQR)	17.25

Descriptive statistics

Standard deviation	41.213218
Coefficient of variation (CV)	2.4141634
Kurtosis	60.710065
Mean	17.071429
Median Absolute Deviation (MAD)	6
Skewness	7.3162912
Sum	6214
Variance	1698.5293
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
1	78	13.0%
2	43	7.1%
3	23	3.8%
8	20	3.3%
5	16	2.7%
7	13	2.2%
10	11	1.8%
4	11	1.8%
21	11	1.8%
6	9	1.5%
Other values (47)	129	21.4%
(Missing)	238	39.5%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
1	78	13.0%
2	43	7.1%
3	23	3.8%
4	11	1.8%
5	16	2.7%
6	9	1.5%
7	13	2.2%
8	20	3.3%
9	9	1.5%
10	11	1.8%

Value	Count	Frequency (%)
400	1	0.2%
365	3	0.5%
102	1	0.2%
96	1	0.2%
86	1	0.2%
75	1	0.2%
74	1	0.2%
65	2	0.3%
62	1	0.2%
61	2	0.3%

skill3
Text

MISSING

Distinct	328
Distinct (%)	59.7%
Missing	53
Missing (%)	8.8%
Memory size	25.6 KiB

Length

Max length	40
Median length	30
Mean length	15.710383
Min length	1

Characters and Unicode

Total characters	8625
Distinct characters	65
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	247 ?
Unique (%)	45.0%

Sample

1st row	Microsoft Dynamics CRM
2nd row	SAP R/3
3rd row	Microsoft Dynamics ERP
4th row	Tax
5th row	Process Modeling

Value	Count	Frequency (%)
business	60	5.8%
management	59	5.7%
microsoft	43	4.2%
analysis	32	3.1%
data	21	2.0%
project	19	1.8%
development	18	1.7%
leadership	17	1.6%
process	15	1.4%
consulting	15	1.4%
Other values (333)	737	71.1%

Most occurring characters

Value	Count	Frequency (%)
e	843	9.8%
n	694	8.0%
i	656	7.6%
s	586	6.8%
a	578	6.7%
t	560	6.5%
	487	5.6%
o	484	5.6%
r	451	5.2%
l	308	3.6%
Other values (55)	2978	34.5%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	8625	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
e	843	9.8%
n	694	8.0%
i	656	7.6%
s	586	6.8%
a	578	6.7%
t	560	6.5%
	487	5.6%
o	484	5.6%
r	451	5.2%
l	308	3.6%
Other values (55)	2978	34.5%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	8625	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
e	843	9.8%
n	694	8.0%
i	656	7.6%
s	586	6.8%
a	578	6.7%
t	560	6.5%
	487	5.6%
o	484	5.6%
r	451	5.2%
l	308	3.6%
Other values (55)	2978	34.5%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	8625	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
e	843	9.8%
n	694	8.0%
i	656	7.6%
s	586	6.8%
a	578	6.7%
t	560	6.5%
	487	5.6%
o	484	5.6%
r	451	5.2%
l	308	3.6%
Other values (55)	2978	34.5%

endorsement3
Real number (ℝ)

HIGH CORRELATION MISSING

Distinct	61
Distinct (%)	16.1%
Missing	223
Missing (%)	37.0%
Infinite	0
Infinite (%)	0.0%
Mean	21.327177

Minimum	1
Maximum	2019
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	25.6 KiB

Quantile statistics

Minimum	1
5-th percentile	1
Q1	2
median	5
Q3	17
95-th percentile	51.1
Maximum	2019
Range	2018
Interquartile range (IQR)	15

Descriptive statistics

Standard deviation	110.56284
Coefficient of variation (CV)	5.1841292
Kurtosis	284.56116
Mean	21.327177
Median Absolute Deviation (MAD)	4
Skewness	16.035856
Sum	8083
Variance	12224.141
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
1	76	12.6%
2	58	9.6%
3	29	4.8%
4	20	3.3%
5	15	2.5%
8	14	2.3%
10	12	2.0%
7	10	1.7%
6	10	1.7%
32	8	1.3%
Other values (51)	127	21.1%
(Missing)	223	37.0%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
1	76	12.6%
2	58	9.6%
3	29	4.8%
4	20	3.3%
5	15	2.5%
6	10	1.7%
7	10	1.7%
8	14	2.3%
9	3	0.5%
10	12	2.0%

Value	Count	Frequency (%)
2019	1	0.2%
400	1	0.2%
365	3	0.5%
106	1	0.2%
98	1	0.2%
96	1	0.2%
82	1	0.2%
75	1	0.2%
73	1	0.2%
64	1	0.2%

skill4
Text

MISSING

Distinct	331
Distinct (%)	60.7%
Missing	57
Missing (%)	9.5%
Memory size	25.6 KiB

Length

Max length	47
Median length	29
Mean length	15.222018
Min length	1

Characters and Unicode

Total characters	8296
Distinct characters	63
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	251 ?
Unique (%)	46.1%

Sample

1st row	Scrum
2nd row	SAP Implementation
3rd row	Oracle 10g
4th row	Financial Audits
5th row	Problem Solving

Value	Count	Frequency (%)
management	85	8.4%
business	55	5.4%
microsoft	33	3.3%
project	31	3.1%
analysis	28	2.8%
data	24	2.4%
process	20	2.0%
it	19	1.9%
strategy	18	1.8%
consulting	16	1.6%
Other values (337)	681	67.4%

Most occurring characters

Value	Count	Frequency (%)
e	854	10.3%
n	681	8.2%
a	593	7.1%
t	580	7.0%
i	572	6.9%
s	523	6.3%
	465	5.6%
o	446	5.4%
r	420	5.1%
c	295	3.6%
Other values (53)	2867	34.6%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	8296	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
e	854	10.3%
n	681	8.2%
a	593	7.1%
t	580	7.0%
i	572	6.9%
s	523	6.3%
	465	5.6%
o	446	5.4%
r	420	5.1%
c	295	3.6%
Other values (53)	2867	34.6%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	8296	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
e	854	10.3%
n	681	8.2%
a	593	7.1%
t	580	7.0%
i	572	6.9%
s	523	6.3%
	465	5.6%
o	446	5.4%
r	420	5.1%
c	295	3.6%
Other values (53)	2867	34.6%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	8296	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
e	854	10.3%
n	681	8.2%
a	593	7.1%
t	580	7.0%
i	572	6.9%
s	523	6.3%
	465	5.6%
o	446	5.4%
r	420	5.1%
c	295	3.6%
Other values (53)	2867	34.6%

endorsement4
Real number (ℝ)

HIGH CORRELATION MISSING

Distinct	53
Distinct (%)	14.7%
Missing	241
Missing (%)	40.0%
Infinite	0
Infinite (%)	0.0%
Mean	209973.15

Minimum	1
Maximum	75792018
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	25.6 KiB

Quantile statistics

Minimum	1
5-th percentile	1
Q1	2
median	6
Q3	17
95-th percentile	52
Maximum	75792018
Range	75792017
Interquartile range (IQR)	15

Descriptive statistics

Standard deviation	3989052.4
Coefficient of variation (CV)	18.997916
Kurtosis	361
Mean	209973.15
Median Absolute Deviation (MAD)	5
Skewness	19
Sum	75800308
Variance	1.5912539 × 10¹³
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
1	65	10.8%
2	47	7.8%
3	33	5.5%
4	19	3.2%
8	17	2.8%
6	15	2.5%
7	15	2.5%
5	15	2.5%
9	10	1.7%
12	7	1.2%
Other values (43)	118	19.6%
(Missing)	241	40.0%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
1	65	10.8%
2	47	7.8%
3	33	5.5%
4	19	3.2%
5	15	2.5%
6	15	2.5%
7	15	2.5%
8	17	2.8%
9	10	1.7%
10	1	0.2%

Value	Count	Frequency (%)
75792018	1	0.2%
2019	1	0.2%
1110	1	0.2%
400	1	0.2%
365	1	0.2%
129	1	0.2%
108	1	0.2%
95	1	0.2%
86	1	0.2%
76	1	0.2%

skill5
Text

MISSING

Distinct	311
Distinct (%)	57.8%
Missing	64
Missing (%)	10.6%
Memory size	25.6 KiB

Length

Max length	54
Median length	33
Mean length	16.501859
Min length	1

Characters and Unicode

Total characters	8878
Distinct characters	62
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	233 ?
Unique (%)	43.3%

Sample

1st row	Critical Thinking
2nd row	SAP
3rd row	Red Hat Linux
4th row	Financial Statement Auditing
5th row	Communication

Value	Count	Frequency (%)
management	84	7.9%
business	50	4.7%
analysis	37	3.5%
microsoft	31	2.9%
it	23	2.2%
project	21	2.0%
strategy	20	1.9%
data	18	1.7%
service	16	1.5%
financial	16	1.5%
Other values (344)	753	70.4%

Most occurring characters

Value	Count	Frequency (%)
e	920	10.4%
n	736	8.3%
a	649	7.3%
t	594	6.7%
i	583	6.6%
	531	6.0%
s	516	5.8%
r	470	5.3%
o	466	5.2%
c	289	3.3%
Other values (52)	3124	35.2%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	8878	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
e	920	10.4%
n	736	8.3%
a	649	7.3%
t	594	6.7%
i	583	6.6%
	531	6.0%
s	516	5.8%
r	470	5.3%
o	466	5.2%
c	289	3.3%
Other values (52)	3124	35.2%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	8878	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
e	920	10.4%
n	736	8.3%
a	649	7.3%
t	594	6.7%
i	583	6.6%
	531	6.0%
s	516	5.8%
r	470	5.3%
o	466	5.2%
c	289	3.3%
Other values (52)	3124	35.2%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	8878	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
e	920	10.4%
n	736	8.3%
a	649	7.3%
t	594	6.7%
i	583	6.6%
	531	6.0%
s	516	5.8%
r	470	5.3%
o	466	5.2%
c	289	3.3%
Other values (52)	3124	35.2%

endorsement5
Real number (ℝ)

HIGH CORRELATION MISSING

Distinct	49
Distinct (%)	13.8%
Missing	246
Missing (%)	40.9%
Infinite	0
Infinite (%)	0.0%
Mean	13.092697

Minimum	1
Maximum	400
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	25.6 KiB

Quantile statistics

Minimum	1
5-th percentile	1
Q1	2
median	5
Q3	14
95-th percentile	47.5
Maximum	400
Range	399
Interquartile range (IQR)	12

Descriptive statistics

Standard deviation	31.267944
Coefficient of variation (CV)	2.3881974
Kurtosis	110.45558
Mean	13.092697
Median Absolute Deviation (MAD)	4
Skewness	9.5858253
Sum	4661
Variance	977.68434
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=49)

Value	Count	Frequency (%)
1	64	10.6%
2	37	6.1%
3	33	5.5%
4	30	5.0%
6	20	3.3%
5	19	3.2%
7	18	3.0%
8	11	1.8%
14	8	1.3%
21	7	1.2%
Other values (39)	109	18.1%
(Missing)	246	40.9%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
1	64	10.6%
2	37	6.1%
3	33	5.5%
4	30	5.0%
5	19	3.2%
6	20	3.3%
7	18	3.0%
8	11	1.8%
9	5	0.8%
10	7	1.2%

Value	Count	Frequency (%)
400	1	0.2%
365	1	0.2%
95	1	0.2%
74	1	0.2%
65	1	0.2%
64	1	0.2%
63	1	0.2%
54	3	0.5%
53	4	0.7%
52	3	0.5%

skill6
Text

MISSING

Distinct	317
Distinct (%)	60.8%
Missing	81
Missing (%)	13.5%
Memory size	25.6 KiB

Length

Max length	54
Median length	30
Mean length	15.287908
Min length	3

Characters and Unicode

Total characters	7965
Distinct characters	60
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	235 ?
Unique (%)	45.1%

Sample

1st row	Microsoft PowerPoint
2nd row	ERP
3rd row	Telecom BSS
4th row	Strategy
5th row	Enterprise Resource Planning (ERP)

Value	Count	Frequency (%)
management	63	6.4%
business	53	5.4%
analysis	38	3.9%
microsoft	29	3.0%
data	28	2.9%
it	18	1.8%
process	15	1.5%
service	13	1.3%
project	13	1.3%
leadership	12	1.2%
Other values (333)	698	71.2%

Most occurring characters

Value	Count	Frequency (%)
e	821	10.3%
n	657	8.2%
a	608	7.6%
i	560	7.0%
s	515	6.5%
t	500	6.3%
	459	5.8%
r	415	5.2%
o	413	5.2%
c	262	3.3%
Other values (50)	2755	34.6%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	7965	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
e	821	10.3%
n	657	8.2%
a	608	7.6%
i	560	7.0%
s	515	6.5%
t	500	6.3%
	459	5.8%
r	415	5.2%
o	413	5.2%
c	262	3.3%
Other values (50)	2755	34.6%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	7965	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
e	821	10.3%
n	657	8.2%
a	608	7.6%
i	560	7.0%
s	515	6.5%
t	500	6.3%
	459	5.8%
r	415	5.2%
o	413	5.2%
c	262	3.3%
Other values (50)	2755	34.6%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	7965	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
e	821	10.3%
n	657	8.2%
a	608	7.6%
i	560	7.0%
s	515	6.5%
t	500	6.3%
	459	5.8%
r	415	5.2%
o	413	5.2%
c	262	3.3%
Other values (50)	2755	34.6%

endorsement6
Real number (ℝ)

HIGH CORRELATION MISSING

Distinct	49
Distinct (%)	14.2%
Missing	256
Missing (%)	42.5%
Infinite	0
Infinite (%)	0.0%
Mean	14.459538

Minimum	1
Maximum	900
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	25.6 KiB

Quantile statistics

Minimum	1
5-th percentile	1
Q1	2
median	5
Q3	12
95-th percentile	46.25
Maximum	900
Range	899
Interquartile range (IQR)	10

Descriptive statistics

Standard deviation	54.33823
Coefficient of variation (CV)	3.7579508
Kurtosis	210.37116
Mean	14.459538
Median Absolute Deviation (MAD)	4
Skewness	13.519066
Sum	5003
Variance	2952.6433
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=49)

Value	Count	Frequency (%)
1	67	11.1%
2	42	7.0%
3	31	5.1%
4	26	4.3%
5	19	3.2%
6	19	3.2%
9	13	2.2%
7	13	2.2%
13	10	1.7%
10	9	1.5%
Other values (39)	97	16.1%
(Missing)	256	42.5%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
1	67	11.1%
2	42	7.0%
3	31	5.1%
4	26	4.3%
5	19	3.2%
6	19	3.2%
7	13	2.2%
8	9	1.5%
9	13	2.2%
10	9	1.5%

Value	Count	Frequency (%)
900	1	0.2%
365	1	0.2%
162	1	0.2%
107	1	0.2%
99	1	0.2%
96	1	0.2%
88	1	0.2%
85	1	0.2%
65	1	0.2%
63	1	0.2%

error
Unsupported

MISSING REJECTED UNSUPPORTED

Missing	602
Missing (%)	100.0%
Memory size	25.6 KiB

jobStartDate
Date

Distinct	95
Distinct (%)	15.9%
Missing	5
Missing (%)	0.8%
Memory size	25.6 KiB

Minimum	1996-11-01 00:00:00
Maximum	2024-05-01 00:00:00

Histogram

Histogram with fixed size bins (bins=50)

jobEndDate
Date

Distinct	588
Distinct (%)	98.5%
Missing	5
Missing (%)	0.8%
Memory size	25.6 KiB

Minimum	2015-06-01 00:00:00
Maximum	2024-06-24 12:46:00.195382

Histogram

Histogram with fixed size bins (bins=50)

jobTenureYears
Real number (ℝ)

HIGH CORRELATION

Distinct	113
Distinct (%)	18.9%
Missing	5
Missing (%)	0.8%
Infinite	0
Infinite (%)	0.0%
Mean	2.600938

Minimum	0.08
Maximum	27.64
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	25.6 KiB

Quantile statistics

Minimum	0.08
5-th percentile	0.39
Q1	1.06
median	1.98
Q3	2.73
95-th percentile	7.98
Maximum	27.64
Range	27.56
Interquartile range (IQR)	1.67

Descriptive statistics

Standard deviation	3.0487808
Coefficient of variation (CV)	1.1721851
Kurtosis	23.704152
Mean	2.600938
Median Absolute Deviation (MAD)	0.75
Skewness	4.2467998
Sum	1552.76
Variance	9.2950642
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0.98	50	8.3%
1.98	39	6.5%
1.39	38	6.3%
2.73	34	5.6%
0.48	28	4.7%
1.48	26	4.3%
2.48	24	4.0%
0.39	23	3.8%
1.73	23	3.8%
1.9	14	2.3%
Other values (103)	298	49.5%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0.08	1	0.2%
0.15	3	0.5%
0.17	2	0.3%
0.23	5	0.8%
0.25	4	0.7%
0.31	1	0.2%
0.34	1	0.2%
0.39	23	3.8%
0.48	28	4.7%
0.5	1	0.2%

Value	Count	Frequency (%)
27.64	1	0.2%
25.31	1	0.2%
24.98	1	0.2%
20.48	1	0.2%
18.48	1	0.2%
18.16	1	0.2%
17.81	1	0.2%
16.39	1	0.2%
16.31	1	0.2%
14.23	1	0.2%

schoolStartDate
Date

MISSING

Distinct	81
Distinct (%)	16.0%
Missing	97
Missing (%)	16.1%
Memory size	25.6 KiB

Minimum	1972-01-01 00:00:00
Maximum	2025-01-01 00:00:00

Histogram

Histogram with fixed size bins (bins=50)

schoolEndDate
Date

MISSING

Distinct	101
Distinct (%)	20.0%
Missing	97
Missing (%)	16.1%
Memory size	25.6 KiB

Minimum	1975-01-01 00:00:00
Maximum	2029-12-01 00:00:00

Histogram

Histogram with fixed size bins (bins=50)

schoolStartDate2
Date

MISSING

Distinct	67
Distinct (%)	18.1%
Missing	232
Missing (%)	38.5%
Memory size	25.6 KiB

Minimum	1970-01-01 00:00:00
Maximum	2024-02-01 00:00:00

Histogram

Histogram with fixed size bins (bins=50)

schoolEndDate2
Date

MISSING

Distinct	89
Distinct (%)	24.1%
Missing	232
Missing (%)	38.5%
Memory size	25.6 KiB

Minimum	1972-01-01 00:00:00
Maximum	2024-06-24 12:46:00.278968

Histogram

Histogram with fixed size bins (bins=50)

degreeCategory
Categorical

Distinct	6
Distinct (%)	1.0%
Missing	0
Missing (%)	0.0%
Memory size	25.6 KiB

Bachelor	241
Diploma	239
High School	61
Unknown	56
Master	4

Length

Max length	11
Median length	8
Mean length	7.7923588
Min length	3

Characters and Unicode

Total characters	4691
Distinct characters	24
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1 ?
Unique (%)	0.2%

Sample

1st row	Bachelor
2nd row	Bachelor
3rd row	Bachelor
4th row	Bachelor
5th row	PhD

Common Values

Value	Count	Frequency (%)
Bachelor	241	40.0%
Diploma	239	39.7%
High School	61	10.1%
Unknown	56	9.3%
Master	4	0.7%
PhD	1	0.2%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
bachelor	241	36.3%
diploma	239	36.0%
high	61	9.2%
school	61	9.2%
unknown	56	8.4%
master	4	0.6%
phd	1	0.2%

Most occurring characters

Value	Count	Frequency (%)
o	658	14.0%
l	541	11.5%
a	484	10.3%
h	364	7.8%
c	302	6.4%
i	300	6.4%
e	245	5.2%
r	245	5.2%
B	241	5.1%
D	240	5.1%
Other values (14)	1071	22.8%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	4691	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
o	658	14.0%
l	541	11.5%
a	484	10.3%
h	364	7.8%
c	302	6.4%
i	300	6.4%
e	245	5.2%
r	245	5.2%
B	241	5.1%
D	240	5.1%
Other values (14)	1071	22.8%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	4691	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
o	658	14.0%
l	541	11.5%
a	484	10.3%
h	364	7.8%
c	302	6.4%
i	300	6.4%
e	245	5.2%
r	245	5.2%
B	241	5.1%
D	240	5.1%
Other values (14)	1071	22.8%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	4691	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
o	658	14.0%
l	541	11.5%
a	484	10.3%
h	364	7.8%
c	302	6.4%
i	300	6.4%
e	245	5.2%
r	245	5.2%
B	241	5.1%
D	240	5.1%
Other values (14)	1071	22.8%

degreeCategory2
Categorical

HIGH CORRELATION

Distinct	7
Distinct (%)	1.2%
Missing	0
Missing (%)	0.0%
Memory size	25.6 KiB

Unknown	243
Diploma	147
Bachelor	145
High School	59
Master	5
Other values (2)	3

Length

Max length	11
Median length	7
Mean length	7.6146179
Min length	3

Characters and Unicode

Total characters	4584
Distinct characters	25
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1 ?
Unique (%)	0.2%

Sample

1st row	Bachelor
2nd row	Bachelor
3rd row	Bachelor
4th row	High School
5th row	Unknown

Common Values

Value	Count	Frequency (%)
Unknown	243	40.4%
Diploma	147	24.4%
Bachelor	145	24.1%
High School	59	9.8%
Master	5	0.8%
PhD	2	0.3%
Associate	1	0.2%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
unknown	243	36.8%
diploma	147	22.2%
bachelor	145	21.9%
high	59	8.9%
school	59	8.9%
master	5	0.8%
phd	2	0.3%
associate	1	0.2%

Most occurring characters

Value	Count	Frequency (%)
n	729	15.9%
o	654	14.3%
l	351	7.7%
a	298	6.5%
h	265	5.8%
U	243	5.3%
k	243	5.3%
w	243	5.3%
i	207	4.5%
c	205	4.5%
Other values (15)	1146	25.0%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	4584	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
n	729	15.9%
o	654	14.3%
l	351	7.7%
a	298	6.5%
h	265	5.8%
U	243	5.3%
k	243	5.3%
w	243	5.3%
i	207	4.5%
c	205	4.5%
Other values (15)	1146	25.0%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	4584	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
n	729	15.9%
o	654	14.3%
l	351	7.7%
a	298	6.5%
h	265	5.8%
U	243	5.3%
k	243	5.3%
w	243	5.3%
i	207	4.5%
c	205	4.5%
Other values (15)	1146	25.0%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	4584	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
n	729	15.9%
o	654	14.3%
l	351	7.7%
a	298	6.5%
h	265	5.8%
U	243	5.3%
k	243	5.3%
w	243	5.3%
i	207	4.5%
c	205	4.5%
Other values (15)	1146	25.0%

fieldCategory
Categorical

Distinct	8
Distinct (%)	1.3%
Missing	0
Missing (%)	0.0%
Memory size	25.6 KiB

Health	210
IT	167
Data	74
Science	60
Unknown	56
Other values (3)	35

Length

Max length	11
Median length	8
Mean length	4.9152824
Min length	2

Characters and Unicode

Total characters	2959
Distinct characters	24
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Health
2nd row	Science
3rd row	IT
4th row	IT
5th row	Business

Common Values

Value	Count	Frequency (%)
Health	210	34.9%
IT	167	27.7%
Data	74	12.3%
Science	60	10.0%
Unknown	56	9.3%
Business	17	2.8%
Arts	11	1.8%
Engineering	7	1.2%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
health	210	34.9%
it	167	27.7%
data	74	12.3%
science	60	10.0%
unknown	56	9.3%
business	17	2.8%
arts	11	1.8%
engineering	7	1.2%

Most occurring characters

Value	Count	Frequency (%)
e	361	12.2%
a	358	12.1%
t	295	10.0%
n	266	9.0%
h	210	7.1%
H	210	7.1%
l	210	7.1%
I	167	5.6%
T	167	5.6%
c	120	4.1%
Other values (14)	595	20.1%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	2959	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
e	361	12.2%
a	358	12.1%
t	295	10.0%
n	266	9.0%
h	210	7.1%
H	210	7.1%
l	210	7.1%
I	167	5.6%
T	167	5.6%
c	120	4.1%
Other values (14)	595	20.1%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	2959	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
e	361	12.2%
a	358	12.1%
t	295	10.0%
n	266	9.0%
h	210	7.1%
H	210	7.1%
l	210	7.1%
I	167	5.6%
T	167	5.6%
c	120	4.1%
Other values (14)	595	20.1%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	2959	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
e	361	12.2%
a	358	12.1%
t	295	10.0%
n	266	9.0%
h	210	7.1%
H	210	7.1%
l	210	7.1%
I	167	5.6%
T	167	5.6%
c	120	4.1%
Other values (14)	595	20.1%

fieldCategory2
Categorical

HIGH CORRELATION

Distinct	9
Distinct (%)	1.5%
Missing	0
Missing (%)	0.0%
Memory size	25.6 KiB

Unknown	243
Health	165
IT	86
Data	32
Science	32
Other values (4)	44

Length

Max length	11
Median length	8
Mean length	5.948505
Min length	2

Characters and Unicode

Total characters	3581
Distinct characters	25
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1 ?
Unique (%)	0.2%

Sample

1st row	Health
2nd row	Engineering
3rd row	IT
4th row	Engineering
5th row	Unknown

Common Values

Value	Count	Frequency (%)
Unknown	243	40.4%
Health	165	27.4%
IT	86	14.3%
Data	32	5.3%
Science	32	5.3%
Business	18	3.0%
Engineering	17	2.8%
Arts	8	1.3%
Law	1	0.2%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
unknown	243	40.4%
health	165	27.4%
it	86	14.3%
data	32	5.3%
science	32	5.3%
business	18	3.0%
engineering	17	2.8%
arts	8	1.3%
law	1	0.2%

Most occurring characters

Value	Count	Frequency (%)
n	830	23.2%
e	281	7.8%
w	244	6.8%
U	243	6.8%
k	243	6.8%
o	243	6.8%
a	230	6.4%
t	205	5.7%
H	165	4.6%
l	165	4.6%
Other values (15)	732	20.4%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	3581	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
n	830	23.2%
e	281	7.8%
w	244	6.8%
U	243	6.8%
k	243	6.8%
o	243	6.8%
a	230	6.4%
t	205	5.7%
H	165	4.6%
l	165	4.6%
Other values (15)	732	20.4%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	3581	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
n	830	23.2%
e	281	7.8%
w	244	6.8%
U	243	6.8%
k	243	6.8%
o	243	6.8%
a	230	6.4%
t	205	5.7%
H	165	4.6%
l	165	4.6%
Other values (15)	732	20.4%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	3581	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
n	830	23.2%
e	281	7.8%
w	244	6.8%
U	243	6.8%
k	243	6.8%
o	243	6.8%
a	230	6.4%
t	205	5.7%
H	165	4.6%
l	165	4.6%
Other values (15)	732	20.4%

weighted_skills
Unsupported

REJECTED UNSUPPORTED

Missing	0
Missing (%)	0.0%
Memory size	25.6 KiB

totalExperienceYears
Real number (ℝ)

MISSING ZEROS

Distinct	83
Distinct (%)	16.3%
Missing	93
Missing (%)	15.4%
Infinite	0
Infinite (%)	0.0%
Mean	10.85057

Minimum	-5.44
Maximum	49.48
Zeros	20
Zeros (%)	3.3%
Negative	9
Negative (%)	1.5%
Memory size	25.6 KiB

Quantile statistics

Minimum	-5.44
5-th percentile	0
Q1	3.48
median	8.48
Q3	16.48
95-th percentile	29.08
Maximum	49.48
Range	54.92
Interquartile range (IQR)	13

Descriptive statistics

Standard deviation	9.373172
Coefficient of variation (CV)	0.86384146
Kurtosis	0.85495125
Mean	10.85057
Median Absolute Deviation (MAD)	6
Skewness	1.0896277
Sum	5522.94
Variance	87.856353
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
3.48	40	6.6%
4.48	30	5.0%
9.48	26	4.3%
2.48	22	3.7%
6.48	22	3.7%
8.48	20	3.3%
0	20	3.3%
7.48	17	2.8%
5.48	17	2.8%
10.48	17	2.8%
Other values (73)	278	46.2%
(Missing)	93	15.4%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
-5.44	1	0.2%
-2.52	1	0.2%
-1.77	1	0.2%
-0.85	1	0.2%
-0.52	2	0.3%
-0.44	2	0.3%
-0.02	1	0.2%
0	20	3.3%
0.15	2	0.3%
0.48	2	0.3%

Value	Count	Frequency (%)
49.48	1	0.2%
43.48	1	0.2%
41.48	2	0.3%
40.48	1	0.2%
39.48	1	0.2%
37.48	1	0.2%
36.48	2	0.3%
34.48	3	0.5%
33.48	1	0.2%
32.48	3	0.5%

jobTenureCategory
Categorical

HIGH CORRELATION

Distinct	3
Distinct (%)	0.5%
Missing	5
Missing (%)	0.8%
Memory size	25.6 KiB

Mid-level	217
Senior	211
Junior	169

Length

Max length	9
Median length	6
Mean length	7.0904523
Min length	6

Characters and Unicode

Total characters	4233
Distinct characters	13
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Junior
2nd row	Junior
3rd row	Mid-level
4th row	Mid-level
5th row	Junior

Common Values

Value	Count	Frequency (%)
Mid-level	217	36.0%
Senior	211	35.0%
Junior	169	28.1%
(Missing)	5	0.8%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
mid-level	217	36.3%
senior	211	35.3%
junior	169	28.3%

Most occurring characters

Value	Count	Frequency (%)
e	645	15.2%
i	597	14.1%
l	434	10.3%
n	380	9.0%
o	380	9.0%
r	380	9.0%
M	217	5.1%
d	217	5.1%
-	217	5.1%
v	217	5.1%
Other values (3)	549	13.0%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	4233	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
e	645	15.2%
i	597	14.1%
l	434	10.3%
n	380	9.0%
o	380	9.0%
r	380	9.0%
M	217	5.1%
d	217	5.1%
-	217	5.1%
v	217	5.1%
Other values (3)	549	13.0%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	4233	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
e	645	15.2%
i	597	14.1%
l	434	10.3%
n	380	9.0%
o	380	9.0%
r	380	9.0%
M	217	5.1%
d	217	5.1%
-	217	5.1%
v	217	5.1%
Other values (3)	549	13.0%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	4233	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
e	645	15.2%
i	597	14.1%
l	434	10.3%
n	380	9.0%
o	380	9.0%
r	380	9.0%
M	217	5.1%
d	217	5.1%
-	217	5.1%
v	217	5.1%
Other values (3)	549	13.0%

Auto

Heatmap
Table

	company	degreeCategory	degreeCategory2	endorsement1	endorsement2	endorsement3	endorsement4	endorsement5	endorsement6	fieldCategory	fieldCategory2	jobTenureCategory	jobTenureYears	jobTitle	totalExperienceYears
company	1.000	0.000	0.409	-0.036	-0.021	-0.035	-0.047	-0.034	-0.077	0.163	0.109	0.076	0.038	0.000	0.037
degreeCategory	0.000	1.000	0.114	0.032	0.030	0.051	0.056	0.094	-0.051	0.491	0.119	0.050	0.057	0.025	-0.116
degreeCategory2	0.409	0.114	1.000	-0.052	-0.034	0.013	-0.050	-0.000	-0.051	0.107	0.506	0.000	-0.012	0.000	0.103
endorsement1	-0.036	0.032	-0.052	1.000	0.723	0.650	0.561	0.558	0.449	0.000	0.080	0.086	0.133	0.081	0.279
endorsement2	-0.021	0.030	-0.034	0.723	1.000	0.695	0.646	0.559	0.514	0.000	0.000	0.000	0.102	0.019	0.292
endorsement3	-0.035	0.051	0.013	0.650	0.695	1.000	0.663	0.601	0.528	0.059	0.000	0.053	0.087	0.082	0.270
endorsement4	-0.047	0.056	-0.050	0.561	0.646	0.663	1.000	0.671	0.647	0.000	0.000	0.000	0.099	0.048	0.258
endorsement5	-0.034	0.094	-0.000	0.558	0.559	0.601	0.671	1.000	0.550	0.159	0.000	0.020	0.077	0.072	0.268
endorsement6	-0.077	-0.051	-0.051	0.449	0.514	0.528	0.647	0.550	1.000	0.000	0.000	0.000	0.105	0.109	0.244
fieldCategory	0.163	0.491	0.107	0.000	0.000	0.059	0.000	0.159	0.000	1.000	0.098	0.095	0.075	0.012	0.087
fieldCategory2	0.109	0.119	0.506	0.080	0.000	0.000	0.000	0.000	0.000	0.098	1.000	0.070	-0.026	0.072	0.145
jobTenureCategory	0.076	0.050	0.000	0.086	0.000	0.053	0.000	0.020	0.000	0.095	0.070	1.000	0.942	0.188	0.356
jobTenureYears	0.038	0.057	-0.012	0.133	0.102	0.087	0.099	0.077	0.105	0.075	-0.026	0.942	1.000	0.136	0.403
jobTitle	0.000	0.025	0.000	0.081	0.019	0.082	0.048	0.072	0.109	0.012	0.072	0.188	0.136	1.000	-0.165
totalExperienceYears	0.037	-0.116	0.103	0.279	0.292	0.270	0.258	0.268	0.244	0.087	0.145	0.356	0.403	-0.165	1.000

A simple visualization of nullity by column.

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

First rows
Last rows

	location	fullName	company	jobTitle	jobLocation	jobDateRange	jobStartedSince	jobDuration	company2	jobTitle2	jobDateRange2	jobStartedSince2	school	schoolUrl	schoolDateRange	schoolUrl2	schoolDegree2	schoolDateRange2	allSkills	skill1	endorsement1	skill2	endorsement2	skill3	endorsement3	skill4	endorsement4	skill5	endorsement5	skill6	endorsement6	error	jobStartDate	jobEndDate	jobTenureYears	schoolStartDate	schoolEndDate	schoolStartDate2	schoolEndDate2	degreeCategory	degreeCategory2	fieldCategory	fieldCategory2	weighted_skills	totalExperienceYears	jobTenureCategory
2	Greater Melbourne Area	Will Adams	KPMG Australia	Senior Consultant	Melbourne, Victoria, Australia	Oct 2020 - Feb 2022	2020-10-01	1 yr 5 mos	KPMG Australia	Consultant	Sep 2018 - Oct 2020	2018-09-01	Monash University	https://www.linkedin.com/company/5663/	2012 - 2016	https://www.linkedin.com/company/5663/	Bachelor's degree, Business (Marketing) (International Business)	2012 - 2016	Management Consulting, Presentations, Microsoft Dynamics CRM, Scrum, Critical Thinking, Microsoft PowerPoint	Management Consulting	4.0	Presentations	7.0	Microsoft Dynamics CRM	5.0	Scrum	4.0	Critical Thinking	4.0	Microsoft PowerPoint	4.0	NaN	2020-10-01	2022-02-01 00:00:00.000000	1.34	2012-01-01	2016-01-01	2012-01-01	2016-01-01	Bachelor	Bachelor	Health	Health	{'Management Consulting': 4.0, 'Presentations': 7.0, 'Microsoft Dynamics CRM': 5.0, 'Scrum': 4.0, 'Critical Thinking': 4.0, 'Microsoft PowerPoint': 4.0}	8.48	Junior
4	Melbourne, Victoria	Ammrith Adithya	KPMG	Consultant	Melbourne, Victoria, Australia	Feb 2024 - Present	2024-02-01	5 mos	Commonwealth Bank	Early Customer Engagement Specialist (Financial Assist Solutions)	Mar 2020 - Aug 2020	2020-03-01	Monash University	https://www.linkedin.com/company/5663/	2019 - 2023	NaN	VCE	2015 - 2018	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2024-02-01	2024-06-24 12:46:00.184354	0.39	2019-01-01	2023-01-01	2015-01-01	2018-01-01	Bachelor	Bachelor	Science	Engineering	{}	1.48	Junior
6	Greater Sydney Area	Prabin Agarwal	KPMG Australia	Associate Director	Sydney, New South Wales, Australia	Mar 2022 - Present	2022-03-01	2 yrs 4 mos	Accenture Australia	Manager	Oct 2016 - Feb 2022	2016-10-01	Institute of Chartered Accountant of India	NaN	1999 - 2004	https://www.linkedin.com/company/633270/	Bachelor of Commerce (B.Com.), Accountancy	1999 - 2002	FICO, ECC, SAP R/3, SAP Implementation, SAP, ERP, SAP FICO, Requirements Analysis, SAP FI, SAP ERP, Business Analysis, IDOC, SAP Products, Consulting, Integration, Financial Reporting, Team Management, Vendor Management	FICO	1.0	ECC	NaN	SAP R/3	1.0	SAP Implementation	1.0	SAP	10.0	ERP	6.0	NaN	2022-03-01	2024-06-24 12:46:00.184378	2.32	1999-01-01	2004-01-01	1999-01-01	2002-01-01	Bachelor	Bachelor	IT	IT	{'FICO': 1.0, 'SAP R/3': 1.0, 'SAP Implementation': 1.0, 'SAP': 10.0, 'ERP': 6.0}	20.48	Mid-level
7	Melbourne, Victoria, Australia	Priyanka Agarwal	KPMG Australia	Associate Director	Melbourne, Victoria, Australia	May 2022 - Present	2022-05-01	2 yrs 2 mos	DXC Technology	Senior Technical Consultant	Sep 2021 - May 2022	2021-09-01	College Of Engineering Roorkee	NaN	2002 - 2006	NaN	SSC, PCM	1999 - 2002	MorphX, X++, Microsoft Dynamics ERP, Oracle 10g, Red Hat Linux, Telecom BSS, Microsoft Dynamics AX 4.0, 2009, 2012, 2012 R2, Enterprise Portals, Oracle, Microsoft Dynamics, SSRS, ERP, Solution Architecture, Business Process, Requirements Analysis, SQL, .NET, Business Analysis, Microsoft SQL Server, Enterprise Resource Planning (ERP)	MorphX	2.0	X++	9.0	Microsoft Dynamics ERP	4.0	Oracle 10g	1.0	Red Hat Linux	2.0	Telecom BSS	2.0	NaN	2022-05-01	2024-06-24 12:46:00.184397	2.15	2002-01-01	2006-01-01	1999-01-01	2002-01-01	Bachelor	High School	IT	Engineering	{'MorphX': 2.0, 'X++': 9.0, 'Microsoft Dynamics ERP': 4.0, 'Oracle 10g': 1.0, 'Red Hat Linux': 2.0, 'Telecom BSS': 2.0}	18.48	Mid-level
8	Delhi, India	Akansha Aggarwal	KPMG	Manager	Delhi, India	Sep 2023 - Present	2023-09-01	10 mos	KPMG	Executive	Sep 2022 - Sep 2023	2022-09-01	The Institute of Chartered Accountants of India	https://www.linkedin.com/company/1968486/	2012 - 2018	NaN	NaN	NaN	External Audits, Auditing, Tax, Financial Audits, Financial Statement Auditing	External Audits	NaN	Auditing	NaN	Tax	NaN	Financial Audits	NaN	Financial Statement Auditing	NaN	NaN	NaN	NaN	2023-09-01	2024-06-24 12:46:00.184415	0.81	2012-01-01	2018-01-01	NaT	NaT	PhD	Unknown	Business	Unknown	{}	6.48	Junior
9	Greater Melbourne Area	Pallavi Aggarwal	KPMG Australia	Manager	NaN	Sep 2022 - Present	2022-09-01	1 yr 10 mos	Infosys Consulting	Consultant	Feb 2021 - Aug 2022	2021-02-01	Birla Institute of Technology and Science, Pilani	https://www.linkedin.com/company/739903/	2004 - 2008	https://www.linkedin.com/company/9584148/	BCA, Computer Applications	2001 - 2004	Business Process Improvement, Technical Specs, Process Modeling, Problem Solving, Communication, Strategy, Technical Architecture, Business Requirements, Architecture, Agile Methodologies, PL/SQL, Java, Testing, Telecommunications, Requirements Analysis, Service Delivery, Integration, User Acceptance Testing, SDP, Solution Architecture, Test Planning, Software Project Management, Test Automation, Software Development, Software Quality Assurance, Oracle, OSS, Open Source Software, Linux, WebLogic, Quality Center, Weblogic, Unix, SQL, JIRA, SoapUI, HP Quality Center	Business Process Improvement	NaN	Technical Specs	43.0	Process Modeling	32.0	Problem Solving	54.0	Communication	21.0	Strategy	NaN	NaN	2022-09-01	2024-06-24 12:46:00.184431	1.81	2004-01-01	2008-01-01	2001-01-01	2004-01-01	Bachelor	Bachelor	IT	Data	{'Technical Specs': 43.0, 'Process Modeling': 32.0, 'Problem Solving': 54.0, 'Communication': 21.0}	16.48	Mid-level
10	Sydney, New South Wales, Australia	Saurabh Agrahari	KPMG Australia	Associate Director	Sydney, New South Wales, Australia · On-site	Apr 2023 - Present	2023-04-01	1 yr 3 mos	Techwave	Director - Data Analytics & EPM	Jun 2020 - Mar 2023	2020-06-01	Great Lakes Institute of Management	https://www.linkedin.com/company/363736/	2007 - 2008	https://www.linkedin.com/company/15109269/	Bachelor of Technology (B.Tech.), Information Technology	1999 - 2003	SAP Datasphere, Pre-sales, Business Analysis, Project Management, Program Management, Enterprise Resource Planning (ERP), Business Intelligence (BI), Data Analysis, Business Analytics, Strategic Planning, Project Planning, Financial Analysis, Financial Planning, Financial Forecasting, FP&A, Software Project Management, Agile Project Management, Cloud Computing, SAP BI, SAP BW, SAP ERP, Business Objects, SAP S/4HANA, SAP Analytics Cloud, SAP Project Management, EPM, SAP BPC, SAP HANA, anaplan, Planning Budgeting & Forecasting, Predictive Analytics, Anaplan, Trusted Business Partner, Interpersonal Skills, Design Thinking, Agile & Waterfall Methodologies, Agile Application Development, Process Improvement, Innovation Management, Problem Solving	SAP Datasphere	NaN	Pre-sales	35.0	Business Analysis	17.0	Project Management	11.0	Program Management	1.0	Enterprise Resource Planning (ERP)	8.0	NaN	2023-04-01	2024-06-24 12:46:00.184447	1.23	2007-01-01	2008-01-01	1999-01-01	2003-01-01	Bachelor	Bachelor	Data	IT	{'Pre-sales': 35.0, 'Business Analysis': 17.0, 'Project Management': 11.0, 'Program Management': 1.0, 'Enterprise Resource Planning (ERP)': 8.0}	16.48	Junior
14	Greater Melbourne Area	Tyler Aitken	KPMG Australia	Manager	Melbourne, Victoria, Australia	Oct 2020 - Present	2020-10-01	3 yrs 9 mos	KPMG Australia	Senior Consultant (Digital Delta)	Oct 2018 - Sep 2020	2018-10-01	Chartered Accountants Australia and New Zealand	https://www.linkedin.com/company/654969/	2018 - 2020	https://www.linkedin.com/company/5663/	Double Degree, Bachelor of Commerce and Bachelor of Business Information Systems	2012 - 2015	Microsoft Office, Finance, Strategy, Analysis, Business Analysis, Financial Analysis, Risk Management, Teamwork, Management, Project Delivery, Business Process Improvement, Microsoft Excel	Microsoft Office	1.0	Finance	1.0	Strategy	1.0	Analysis	1.0	Business Analysis	1.0	Financial Analysis	NaN	NaN	2020-10-01	2024-06-24 12:46:00.184463	3.73	2018-01-01	2020-01-01	2012-01-01	2015-01-01	Bachelor	Bachelor	Health	Health	{'Microsoft Office': 1.0, 'Finance': 1.0, 'Strategy': 1.0, 'Analysis': 1.0, 'Business Analysis': 1.0}	4.48	Senior
15	Greater Melbourne Area	Luis Ajero	KPMG Australia	Senior Consultant	Melbourne, Victoria, Australia	Apr 2022 - Present	2022-04-01	2 yrs 3 mos	Appirio	Associate Technical Consultant	Oct 2019 - Apr 2022	2019-10-01	University of Melbourne	https://www.linkedin.com/company/5677/	Feb 2015 - Nov 2018	NaN	NaN	NaN	Python, JavaScript, Java, Microsoft Office, Microsoft PowerPoint, HTML, Git, NodeJs, SQL, English, Haskell, Mean Stack, Express.js	Python	1.0	JavaScript	2.0	Java	2.0	Microsoft Office	1.0	Microsoft PowerPoint	1.0	HTML	2.0	NaN	2022-04-01	2024-06-24 12:46:00.184478	2.23	2015-02-01	2018-11-01	NaT	NaT	Diploma	Unknown	IT	Unknown	{'Python': 1.0, 'JavaScript': 2.0, 'Java': 2.0, 'Microsoft Office': 1.0, 'Microsoft PowerPoint': 1.0, 'HTML': 2.0}	5.65	Mid-level
16	Greater Sydney Area	Philip Alava	KPMG Australia	Senior Consultant	NaN	Jul 2023 - Present	2023-07-01	1 yr	KPMG Australia	Senior Consultant \| Transformation Program Management	Jun 2022 - Jul 2023	2022-06-01	UNSW	https://www.linkedin.com/company/6096/	2018 - 2021	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2023-07-01	2024-06-24 12:46:00.184494	0.98	2018-01-01	2021-01-01	NaT	NaT	Bachelor	Unknown	IT	Unknown	{}	3.48	Junior

	location	fullName	company	jobTitle	jobLocation	jobDateRange	jobStartedSince	jobDuration	company2	jobTitle2	jobDateRange2	jobStartedSince2	school	schoolUrl	schoolDateRange	schoolUrl2	schoolDegree2	schoolDateRange2	allSkills	skill1	endorsement1	skill2	endorsement2	skill3	endorsement3	skill4	endorsement4	skill5	endorsement5	skill6	endorsement6	error	jobStartDate	jobEndDate	jobTenureYears	schoolStartDate	schoolEndDate	schoolStartDate2	schoolEndDate2	degreeCategory	degreeCategory2	fieldCategory	fieldCategory2	weighted_skills	totalExperienceYears	jobTenureCategory
1060	Greater Sydney Area	Ricky Yuen	KPMG	Consultant	NaN	Feb 2023 - Present	2023-02-01	1 yr 5 mos	EY	Audit & Assurance Vacationer at EY	Jan 2022 - Feb 2022	2022-01-01	UNSW	https://www.linkedin.com/company/6096/	2018 - 2022	NaN	NaN	NaN	Interpersonal Skills, Time-efficient	Interpersonal Skills	2.0	Time-efficient	2.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2023-02-01	2024-06-24 12:46:00.195235	1.39	2018-01-01	2022-01-01	NaT	NaT	Bachelor	Unknown	IT	Unknown	{'Interpersonal Skills': 2.0, 'Time-efficient': 2.0}	2.48	Mid-level
1061	Greater Sydney Area	Sumedha Zadoo	KPMG Australia	Associate Director	Sydney, New South Wales, Australia	Oct 2021 - Present	2021-10-01	2 yrs 9 mos	KPMG Australia	Manager \| Management Consulting	Oct 2019 - Sep 2021	2019-10-01	University of Technology Sydney	https://www.linkedin.com/company/166678/	2014 - 2015	NaN	Master of Engineering Management, Engineering Management	2013 - 2014	Requirements Gathering, Management Consulting, Requirements Analysis, Agile Methodologies, Business Strategy, Use Case Analysis, Microsoft SQL Server, Business Process Improvement, FRD, BRDs, Workshop Facilitation, Lean Six Sigma, Project Management, SQL, Management, ASP.NET, Testing, Data Analysis, Databases, MS Project, Project Planning, SDLC, Business Planning, Microsoft BI Suite, Digital Marketing, Market Research, Business Intelligence, SSRS, Primavera P6, Microsoft Project, HTML, MySQL, PL/SQL, Microsoft Office, Microsoft Excel	Requirements Gathering	19.0	Management Consulting	11.0	Requirements Analysis	19.0	Agile Methodologies	11.0	Business Strategy	8.0	Use Case Analysis	5.0	NaN	2021-10-01	2024-06-24 12:46:00.195250	2.73	2014-01-01	2015-01-01	2013-01-01	2014-01-01	Bachelor	Diploma	IT	Health	{'Requirements Gathering': 19.0, 'Management Consulting': 11.0, 'Requirements Analysis': 19.0, 'Agile Methodologies': 11.0, 'Business Strategy': 8.0, 'Use Case Analysis': 5.0}	9.48	Senior
1066	Canberra, Australian Capital Territory, Australia	Michelle Zeitlhofer	KPMG Australia	Consultant	Canberra, Australian Capital Territory, Australia	Feb 2023 - Present	2023-02-01	1 yr 5 mos	Hireup	Disability Support Worker	Jan 2019 - Feb 2023	2019-01-01	University of Canberra	https://www.linkedin.com/company/19034/	2019 - 2022	https://www.linkedin.com/company/19034/	Bachelor's degree, Event and Tourism Management	2019 - 2022	Microsoft Office, Volunteering, Customer Service, Communication, Professional Development	Microsoft Office	NaN	Volunteering	NaN	Customer Service	NaN	Communication	1.0	Professional Development	NaN	NaN	NaN	NaN	2023-02-01	2024-06-24 12:46:00.195265	1.39	2019-01-01	2022-01-01	2019-01-01	2022-01-01	High School	Diploma	Health	Health	{'Communication': 1.0}	2.48	Mid-level
1067	Sydney, New South Wales, Australia	Charmaine Zets	KPMG	Director	NaN	Oct 2023 - Present	2023-10-01	9 mos	KPMG	Digital Transformation Lead	Feb 2021 - Sep 2023	2021-02-01	AGSM @ UNSW Business School	https://www.linkedin.com/company/15104530/	2009 - 2012	NaN	Certification, Agile	2017 - 2017	Business Transformation, Program Management, Risk Management, Project Management, Performance Management, Business Process Improvement, Financial Analysis, Business Strategy, Financial Modeling, Change Management, Strategic Planning, Management Consulting, Project Coordination, Strategy, Leadership, Stakeholder Engagement, Stakeholder Management, Digital transformation	Business Transformation	16.0	Program Management	15.0	Risk Management	12.0	Project Management	13.0	Performance Management	8.0	Business Process Improvement	12.0	NaN	2023-10-01	2024-06-24 12:46:00.195279	0.73	2009-01-01	2012-01-01	2017-01-01	2017-01-01	Bachelor	Bachelor	IT	Engineering	{'Business Transformation': 16.0, 'Program Management': 15.0, 'Risk Management': 12.0, 'Project Management': 13.0, 'Performance Management': 8.0, 'Business Process Improvement': 12.0}	12.48	Junior
1069	Greater Sydney Area	Phillip Zhang	KPMG Australia	Senior Consultant	NaN	Jan 2024 - Present	2024-01-01	6 mos	KPMG Australia	Consultant	Feb 2022 - Jan 2024	2022-02-01	UNSW	https://www.linkedin.com/company/6096/	2017 - 2021	NaN	NaN	2011 - 2016	Leadership, Teamwork, Critical Thinking, Agile Project Management, Financial Analysis, Client Engagement, Microsoft Office, Microsoft Excel, Microsoft PowerPoint, Microsoft Word, Communication, Time Management, Decision-Making, Customer Service, Agile Leadership, Interpersonal Skills, Public Speaking, Wellness, Presentations	Leadership	9.0	Teamwork	10.0	Critical Thinking	5.0	Agile Project Management	NaN	Financial Analysis	1.0	Client Engagement	1.0	NaN	2024-01-01	2024-06-24 12:46:00.195308	0.48	2017-01-01	2021-01-01	2011-01-01	2016-01-01	Diploma	Unknown	IT	Unknown	{'Leadership': 9.0, 'Teamwork': 10.0, 'Critical Thinking': 5.0, 'Financial Analysis': 1.0, 'Client Engagement': 1.0}	3.48	Junior
1070	San Francisco Bay Area	Stephanie Zhang	KPMG US	Consultant	NaN	Oct 2022 - Present	2022-10-01	1 yr 9 mos	KPMG US	Senior Associate Advisory	Oct 2018 - Oct 2022	2018-10-01	Ichec Brussels Management School	https://www.linkedin.com/company/15092365/	NaN	https://www.linkedin.com/company/3084/	Bachelor of Science (B.S.), Accounting and Business Administration \| Minor: Enterprise Information Systems	NaN	Accounting, Teamwork, Time Management, Financial Accounting, Events, Event Planning, Invoicing, Access, Microsoft Office, Microsoft Excel, Microsoft Word, PowerPoint, SAP FICO, SAP ERP, SAP MM, Microsoft Dynamics, Visio, Financial Analysis, Bookkeeping, Financial Reporting, Social Media, Research, Marketing, Salesforce.com, Photoshop	Accounting	11.0	Teamwork	12.0	Time Management	13.0	Financial Accounting	6.0	Events	4.0	Event Planning	7.0	NaN	2022-10-01	2024-06-24 12:46:00.195323	1.73	NaT	NaT	NaT	NaT	Bachelor	Bachelor	IT	IT	{'Accounting': 11.0, 'Teamwork': 12.0, 'Time Management': 13.0, 'Financial Accounting': 6.0, 'Events': 4.0, 'Event Planning': 7.0}	NaN	Mid-level
1071	Sydney, New South Wales, Australia	Elian Zheng	KPMG Australia	Senior Consultant	NaN	Jul 2022 - Present	2022-07-01	2 yrs	KPMG Australia	Consultant \| Data & Cloud	Feb 2021 - Jul 2022	2021-02-01	UNSW	https://www.linkedin.com/company/6096/	2015 - 2020	NaN	NaN	2009 - 2014	Teamwork, Leadership, Communication, Microsoft Office, Management, Customer Service, Research, C, Critical Thinking, Presentation Skills, Problem Solving, Data Analysis, Microsoft Excel, Office 365, Spreadsheets	Teamwork	10.0	Leadership	9.0	Communication	9.0	Microsoft Office	3.0	Management	4.0	Customer Service	2.0	NaN	2022-07-01	2024-06-24 12:46:00.195336	1.98	2015-01-01	2020-01-01	2009-01-01	2014-01-01	High School	Unknown	Health	Unknown	{'Teamwork': 10.0, 'Leadership': 9.0, 'Communication': 9.0, 'Microsoft Office': 3.0, 'Management': 4.0, 'Customer Service': 2.0}	4.48	Mid-level
1075	Jing'an District, Shanghai, China	Cathy Zhou	KPMG China	Senior Consultant	Shanghai City, China	Feb 2017 - Present	2017-02-01	7 yrs 5 mos	KPMG US	Director	Jun 2005 - Jan 2017	2005-06-01	Baruch College	https://www.linkedin.com/company/4365/	NaN	https://www.linkedin.com/company/91038/	Bachelor's Degree, Accounting	NaN	AML, OFAC, Economic Sanctions, FCPA, Tax, Financial Services, Due Diligence, Risk Management	AML	1.0	OFAC	3.0	Economic Sanctions	1.0	FCPA	2.0	Tax	NaN	Financial Services	NaN	NaN	2017-02-01	2024-06-24 12:46:00.195351	7.39	NaT	NaT	NaT	NaT	Diploma	Diploma	Health	Health	{'AML': 1.0, 'OFAC': 3.0, 'Economic Sanctions': 1.0, 'FCPA': 2.0}	NaN	Senior
1076	Xuhui District, Shanghai, China	Irene Zhou	KPMG China	Consultant	Shanghai	Oct 2012 - Present	2012-10-01	11 yrs 9 mos	HSBC Commercial Banking	Credit Risk Management intern	Sep 2011 - Dec 2011	2011-09-01	Fudan University	https://www.linkedin.com/company/22859/	2008 - 2012	NaN	NaN	2005 - 2008	CICPA, Finance, Auditing, Internal Controls, IFRS, Analysis, Sarbanes-Oxley Act, Accounting, Management, GAAP, Financial Analysis	CICPA	NaN	Finance	NaN	Auditing	NaN	Internal Controls	NaN	IFRS	NaN	Analysis	NaN	NaN	2012-10-01	2024-06-24 12:46:00.195367	11.73	2008-01-01	2012-01-01	2005-01-01	2008-01-01	Bachelor	Unknown	Engineering	Unknown	{}	12.48	Senior
1077	Sydney, New South Wales, Australia	Ryan Zhu	KPMG Australia	Manager	Australia	May 2022 - Present	2022-05-01	2 yrs 2 mos	DoorDash	Software Engineer	Nov 2021 - May 2022	2021-11-01	University of Sydney	https://www.linkedin.com/company/166676/	2006 - 2007	https://www.linkedin.com/company/9727/	Master of Science - MS, Information and Communication Technology	2005 - 2006	MobX, GatsbyJS, Adobe Experience Manager (AEM), React.js, Java, Business Analysis, Project Management, Node.js, Webpack, Spring, Amazon Web Services (AWS), JavaScript, AngularJS, jQuery, Microsoft SQL Server, MySQL, CSS, HTML, SOAP, XML, J2EE Application Development, Axis2, Technical Data Analysis, TypeScript, Amazon Dynamodb, Serverless, Bootstrap, GraphQL	MobX	1.0	GatsbyJS	3.0	Adobe Experience Manager (AEM)	3.0	React.js	9.0	Java	8.0	Business Analysis	5.0	NaN	2022-05-01	2024-06-24 12:46:00.195382	2.15	2006-01-01	2007-01-01	2005-01-01	2006-01-01	Diploma	Diploma	IT	Health	{'MobX': 1.0, 'GatsbyJS': 3.0, 'Adobe Experience Manager (AEM)': 3.0, 'React.js': 9.0, 'Java': 8.0, 'Business Analysis': 5.0}	17.48	Mid-level

Overview

Variables

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Common Values

Length

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Common Values

Length

Common Values (Plot)

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Most occurring characters

Most occurring categories

Most frequent character per category